AI tips & trends

If improving revenue retention is a key priority in FY25, here is some food for thought. If you believe data is the essential foundation for improving retention, imagine the possibilities with 50-100x more data about your customers. Here’s the thing: Every business has this customer data, but 99% of businesses are sleeping on a data set that could change their business. It’s the unstructured data that’s sitting in ticketing systems, CRMs, chat systems, surveys, and the biggest silo by volume - corporate email systems. Most of us still rely on structured data like usage, click rates, and engagement logs to gauge our customers' health. However, structured data provides only a partial view of customer behavior and revenue drivers. Unstructured data—like customer emails, chats, tickets, and calls —holds the most valuable insights that, when leveraged, will significantly improve revenue outcomes.

Why Unstructured Data is Essential for Revenue GrowthImproving Customer Retention: Unstructured data helps businesses identify early warning signs of dissatisfaction, allowing them to create proactive interventions before customers churn. Repeated mentions of poor experiences, response lags, product-related frustration, and more in call transcripts, cases, and emails indicate potential churn risks. By identifying these trends while they are trending, businesses will improve retention.

Fueling Product Innovation: Let’s face it: Our customers bought a product or service. Post-sales teams don’t develop products and are limited in what they can directly impact. Product teams need more unbiased, unfiltered contextual customer data, and they need it consistently. Unstructured data provides real-time feedback on how customers use products and services. Businesses can analyze customer feedback from multiple channels to identify recurring requests and pain points. This data fuels product innovation and informs customer-led roadmaps that lead to higher engagement rates and more profound value. Developing products that directly respond to customer feedback leads to faster adoption, better advocacy, and a competitive advantage.

Identifying Expansion Opportunities: Unstructured data reveals customer needs and preferences that structured data often overlooks. Businesses can uncover untapped expansion opportunities by analyzing email, chats, and case feedback. These insights help identify additional products or services that interest customers, leading to new upsell or cross-sell possibilities. To drive immediate improvements in revenue retention, the key isn't pouring resources into complex churn algorithms, chatbots, or traditional customer success platforms—it's being more creative with the data you're already collecting. Start listening more closely to your customers, identify the patterns in their pain points, and share this knowledge with your peers who can improve your offerings. This is the year to start thinking outside of the box.

Last year, I bought a pair of ski pants and the zipper fell out on the first chair lift. I called Burton, and they offered an exchange. New pants, first chair, same problem. Support informed me that I was required to return the pants for repair. The repairs would be completed after ski season. For the inconvenience, Burton offered me a 20% discount on my next purchase of skiwear. The next time I am in the market for skiwear that I can't wear during ski season, I will use that coupon.

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I started my first business over 25 years ago. Since that day, I have lived in an almost constant state of fear that somehow, somewhere, things would get so broken that we'd treat a customer like this.

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Let's be clear, no one who runs a business wants stuff like this to happen. Yet, it happens all the time.

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If you run a software company, your engineering team will have usage tools and server logs to tell you when your product is "down" or running slowly. They can report which features are being used and which ones aren't. You'll learn that certain features in your product cost more to run than others, maybe because of a bad query, code, or something else. And you'll know what needs to be upgraded.

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However, every time a customer contacts a business, they are "using" (or "testing") your product. If you sell ski pants, your product is ski pants, and your customer service team. If you sell software, your product is your tech and your customer service.

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Yet, your customer-facing teams have very poor usage data, if any at all. Which feature of our service gets used the most (billing, success, support)? What are the common themes? Is one group working more effectively than the others? Does a team need an upgrade? 

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(BTW, what costs more, your AWS bill or your payroll?)

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The reason your customer-facing teams don't have usage data is because this data is "unstructured," and it is everywhere. Imagine if your engineering team needed to check 50 email inboxes, 1,000 phone recordings, a CRM, and a ticket system to get your product usage statistics. 

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That's where your customer-facing teams are today. Until you can get answers from these systems as easily as an engineer can, you’ll continue to churn, annoy customers, and try to hire your way out of a retention problem. It won’t work.

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The question is no longer about whether you will use AI; it’s when. And no matter where you are on your journey, navigating the ethical implications of AI use is crucial. Ethical AI is not just a buzzword but a set of principles designed to ensure fairness, transparency, and accountability in how businesses use artificial intelligence. In the case of Sturdy, we’ve made ethical AI a core commitment. These principles guide our every move, ensuring AI benefits businesses without crossing the line into unmitigated risk.

What Is Ethical AI?

Ethical AI refers to developing and deploying AI systems that prioritize fairness, transparency, and respect for privacy. For businesses, this means using AI to make smarter decisions while ensuring that the data and technologies used do not cause harm or reinforce biases. The importance of this cannot be overstated—AI has the potential to either empower or exploit, and ethical guidelines ensure we remain on the right side of that divide.

Sturdy’s Commitment to Ethical AI

Sturdy's approach to AI revolves around several inviolable principles:

1. Business-Only Data Use: Sturdy’s AI systems focus solely on improving how businesses make decisions. They don't delve into personal data or manipulate information for other purposes. The data processed by Sturdy comes from business sources like support tickets, corporate emails, or recorded calls—never from personal channels.
2. No Ulterior Motives for Data: The data collected by Sturdy is knowingly provided by our customers, and the company doesn't use this data for any purpose beyond what's agreed upon. This ensures transparency and trust between the platform and its users.
3. Privacy and Protection: One of the most critical aspects of Sturdy’s approach is its commitment to not allowing any entity—whether a business or government—to use its technology in ways that violate privacy. If a client were found to be doing so, Sturdy would terminate the relationship.
4. No Deception: Our product is engineered to prevent deception. It never manipulates or deceives users, ensuring that the insights drawn from AI are used to enhance business practices rather than exploit loopholes.

Human Oversight and the Role of AI

At the core of Sturdy’s AI principles is the belief that AI should not replace human decision-making but augment it. Our Natural Language Classifiers (NLCs) are built to detect risks and opportunities based on the probability that a conversation indicates a particular issue. For example, when a customer complains about a "buggy" product, Sturdy’s AI might tag it as a "Bug" and label the customer as "Unhappy." However, humans remain in control—analyzing the situation and deciding the best action.

Final Thoughts

Sturdy's approach to AI exemplifies how businesses can responsibly use technology to drive growth and improve operations while safeguarding ethics. They demonstrate that AI doesn’t need to infringe on privacy or replace human decision-making. Instead, AI should be a tool that empowers teams, ensures transparency, and upholds ethical standards. Navigating the ethics of AI is not just a challenge—it’s an ongoing commitment, and Sturdy is setting a new standard for how it should be done.

(The image attached to this post is not entirely accurate but read on, and I’ll explain)

I’ve been spending a lot of time on Sturdy’s brand message lately. Part of this process entails interviewing folks from various walks of life about the current state of their businesses, their teams, and the companies they invest in.

The recurring theme: Sales Leaders aren’t having a good time right now. But you knew that already. I want to talk about what you don’t know.

After one of my interviews, I received a text with a quote by the former CEO of Swedish Airlines, Jan Carlzon.

An individual without information can’t take responsibility. An individual with information can’t help but take responsibility.

There are many different “things”’ that impact revenue: bad service, confusing products, poor response times, overselling, bug reports, price, whacky renewal processes, etc. You already knew this.

You know a lot about economic conditions, because that information is widely, and publicly available. You probably know a fair amount about “Sales Things” because your team is talking about “percent to goal” in almost every meeting, and there are a lot of discussions about what’s working and what isn’t. And, you likely review almost every deal in your pipeline.

What would happen if the opportunities in your pipeline were randomly placed in your ticket systems, CRMs, and a smattering of email inboxes? Knowing what was working with sales would get more difficult, if not impossible.

Today, the issues that affect service, product, marketing, etc., are randomly smattered across every customer-facing system in your business. The only way you “know” they happen is if someone else decides they are important enough to log or forward.

How do you get the information you need to make an impact?

Where is the information your product team needs to know?

Where is the information that your pricing team needs to know?

Where is the information that your renewals team needs to know?

If your Product Marketing Manager wants to know how their new pricing plan is working, what would inform that? A pretty good source—I’d argue the best source—of that information is sitting in emails, tickets, and call transcripts. But, if you are a Product Marketing Manager, you don’t have access to tickets, call transcripts, or customer emails.

You’ve been paywalled.

If you want to know what features to fix, there’s a data point in your Support Chatbot. When your Renewals Manager needs information on an account , they need to scroll through tickets and ask a few people, “What’s going on with this account?”

As a result, every business has smart people who rely on other people to log things, categorize things, and forward things. This is why our teams have logins to systems they seldom use -  so they can find a “thing” they might need.

The irony is that the information you need to know to do your job effectively is harder to source than the information about things you can’t control.

You probably know the inflation rate. If you don’t, you can discover it in one search.

Your VP of CS probably doesn’t know “What’s the most common source of customer frustration in the last 90 days?” Why? Because that information is splashed across your business in a host of silos that VP can’t access. Imagine trying to do that job, without that answer.

Imagine if that VP could answer that question in one search, using what customers are actually saying to every person in your business.

This paywalling has made our businesses fragile and slow. The hints of the B2B slowdown were arriving at our doorsteps in emails and tickets for months. “We’re cutting costs”. “Procurement wants a discount.” Why didn’t we see this coming? Because we weren’t looking for it, and couldn’t find it.

Time to get faster, and sturdier. You have smart people who can take responsibility. Bust the paywalls and give them information they need to react and act.

Do that hard things,

Steve

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There has been, and should be, a lot of talk about Ethical AI. Over the last several weeks, I have been revising Sturdy’s Ethical AI policy. I am trying to convey that we don’t do shady stuff and won’t let our customers do it, either.

(If you are interested in Ethical AI, we have a webinar coming up at the end of the month; the registration link is in the comments)

Writing the policy, I realized we need to talk about ethics writ large, not just as it relates to AI.

Consider the case of Allstate and Arity, as reported in a June 9 NYT story, “Is Your Driving Being Secretly Scored?”  Allstate apparently owns Arity. Arity builds phone apps for things like finding gas stations. Their apps also track how you drive, although they bury that minor detail in their “consent” pages (that no one reads). They then share this data with Allstate.

Not a lot of gray area here. This is unethical.

My co-founder, Joel Passen, coined this mantra at our first startup 20’ish years ago:

“Build what you’d want to use, sell it how you’d want to be sold, and service it how you’d want to be serviced.”

I don’t think anyone downloading a Gas Station finder app wants their driving to be sent to Allstate. I would not. And I would not build it.

So, instead of an “Ethical AI” policy, I’ve decided we need an “Ethical Software Policy”. It will encompass our use of AI, our platform, and how we expect our software to be used.

Here’s a bit of a summary so far…

Sturdy’s Ethical Software Policy (WIP):

• Our product is only be used to improve how businesses make decisions so they can be better vendors to their customers;
• We will not support use cases that do not directly relate to our problem set. The use cases for our product will be obvious;
• We do not have ulterior motives for our customer’s data or their users;
• We will not let any entity, business, government, or person use our product in a way that violates a person’s privacy;
• We will not, nor will we allow our product to score or rank human beings;
• Our product will be engineered to prevent deception and must never be used to deceive people;
• Finally, If we feel that one of our customers is using our product in a way that violates our principles, we will terminate their service.

The problem is that many “Ethical Policies” are only as good as the paper they are written on. They are a checkbox on an RFP. None of us want to live in this world. Maybe it's time to try and live in a better one.

At some point, somewhere along the corporate food chain, executives need to say, “No.”

It is hard to say “no” to revenue. Do the hard things.

Let me know your thoughts.

Steve

Years back I had an idea that every time a customer expressed some sort of "love" we would reach out and ask them to be a reference. The way this was supposed to work was that the Support/CS person would forward any happy customer to the marketing team as a "Reference Lead.” Then, marketing would reach out to the customer. Nothing groundbreaking here. If your business doesn't already do this, go ahead and give it a shot. Happy customers close deals for you.

And at the end of the first month, nothing. Why?

Do none of our customers like us?

Did our Support Team drop the ball?

Did the Marketing team drop the ball?

Did the customer refuse?

If you manage groups of people, you can certainly think of other examples.

Like, "Whenever there is a new customer contact, make sure you log it to Salesforce, dang it!"

Or, "Whenever there is a bug report, log it to JIRA."

The reference harvesting failure has stuck with me. It was so simple, yet it failed spectacularly.

I have three takeaways from this that guide me today:

First, in our world of "Knowledge Work" almost every new policy/idea requires a new manual task. Add it to Excel. Track it in CRM. I would say we've built an entire ecosystem centered on digital logging, but it is more like a multiverse. Every silo has its own physics with its own rules and workflows.

Second, every ‘silo-bounce’ increases the failure rate. "Take this thing from Support and log it for the Product Manager so they can recommend it to Engineering." Boing. Boing. Crash. Intersections are more dangerous than freeways.

Finally, whenever you implement a policy, it will fail unless you lean in and check on it regularly, and you probably won't. No coach, no team.

The future will be a much better place for your co-workers and customers.

Artificial Intelligence, after you do the hard things like building integrations, cleaning data, de-duping, creating a UI and then a data-API, will improve your business, your customers, and your life.

There will be no more manual logging. There is no need to ask someone to forward an event.

Your coworkers won't have the soul-sucking task of "logging it if it is important." Your customers won’t email managers, "No one has gotten back to me."

Until that time...

Tomorrow, your team will be assigned a new task to log something for someone else's team. Some people will forget. The other team will be required to read that information. Some people won't do it.

In three months, your CEO will be annoyed. "What ever happened with that one thing I asked for?"

This is one of the reasons my team and I started building Sturdy in 2019. There are too many people logging minutiae so that someone might find the time to read it. There are too many customers that fall through the cracks that could easily be saved. There are too many good ideas that die because of failed execution and lack of accountability.

It doesn't have to be this way.

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I have always been fascinated by how product roadmaps are maintained. So much so that I feel it necessary to pen a bombastic screed on the topic.

(As an aside, when you talk to VC’s, they’ll ask, “What’s your {2-5} year roadmap?” I want to say, “Whatever needs to get built,” but I think better of it. Life Pro Tip: use words like, “disintermediate.”

I find there is little utility in years-long product roadmaps. Unless you ignore your users/customers. If you have a team conducting market research to determine what to build and then put it in a 2-year plan, then you’re ignoring your users. If you have a team advocating for your users and having hard conversations with engineering and sales, you are not ignoring your users.

This is why Gmail, 20 years later, still has the attachments at the bottom of the email instead of at the top, where they belong: the revenue team is filling the roadmap with better ways to sell your data. I digress.)

The three drivers of a company’s product roadmap are:

Things users want;

Things your sellers want;

Things your product team/engineers want.

They don’t overlap as often as you might think.

Your users want usability (and probably a ton of user-permissions stuff). They bought your product missing certain features, and they are OK with that. They primarily want your existing stuff to get better, easier to use, and easier to get data from.

Your sellers want new features. They usually want the best feature that your competitors already have.

Your product team is more complicated. Most teams want insane reliability, security, and speed. Teams run by CTO’s aspiring to wear black turtlenecks build their own UI framework from scratch so that the one thing the new thing does will be 1% better at something.

Where do they overlap?

1. Your Revenue Teams and Users overlap around UI and reporting. If it looks pretty and has cool reports, it will sell software (1).
2. Users and Engineering overlap in the desire for performance and reliability (2).
3. Development and Revenue overlap at shiny things (3). When you hear “Minimally Viable Product,” you’ve found it. When you hear “App Store”, or “I took some screenshots,” you’ve found it.
4. If you are wondering what happens when they all intersect, I don’t know. I can’t remember all three teams agreeing on a feature.

Your existing customers don’t care about shiny things. But you need to grow revenue, and the CTO is on board, so guess what gets built?

(I would like to say that building shiny things isn’t wholly a bad idea. You need to go for it every now and then. Sometimes, really cool stuff gets built. But, in my experience, that shiny MVP is going to the back of the update line the day it's shipped, and it will suck, forever. Related to this is why your “Admin” area is terrible. Don’t lie, you know it is.)

I have sat in so many board meetings where the CTO presents a roadmap, and the COO/Customer Leader freaks out. I was in an amazing one over a decade ago when the CTO’s priority was “voice enabling the product.”

Everyone blew a gasket.

If your customer falls in the woods, and no one is listening, do they make a sound?

If a user reports a bug or asks for a feature, if someone remembers to do it, it  will be manually logged in a drop-down menu in some silo. It’s also probably logged by someone who has no incentive other than to close the ticket as quickly as possible. In other words, if it gets logged, it will be stored somewhere that’s hard to get to, and no one will read it.

If a user is confused, or says something sucks, someone wraps the user in a warm blanket of apologies and moves on. In the worst case scenario, the user will get something like, “that’s actually how we intended it to work!”

(Once, in a design review, a UI team told me they hid a feature because they didn’t want the users to actually use it. It allowed people to opt in to having a paper check instead of a direct deposit. “How many support tickets did this cause last month?” No one knew.)

It takes hard work to know what the customer wants, or hates. It also requires honesty, and a bit of self-flagellation.

I ran into a CxO who wanted AI to “automatically write knowledge base articles.” I hear this as, “Our product is so confusing that we can’t manage the number of questions about how to use it.”

Get honest: fix the product. No one, ever, renewed because of an awesome knowledge base. Good products don’t need AI knowledge bases. They also don’t need churn prediction or quarterly business reviews, but that’s for another time.

To break this cycle, you must be rigorous about logging every feature request, bug, and UI issue. You’ll need to understand why customers are saying, “how do I do this?” and “that’s confusing.”

(Another data point: track when your people apologize. “What are we apologizing for?”)

How will you gather this brutal truth? You need to put someone in charge of collecting data from your 5-50 systems, organizing it by account, and attaching a cost-benefit analysis to each issue. Then put it in a spreadsheet and review it every week with the Revenue, Ops, Customer and Engineering teams. Soon everyone will develop a healthy anxiety about the quality of your product. Saying “no” to shiny things will get easier.

Do this and your customers will like you again.

End rant.

Do the hard things,

Steve

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I spoke to an entrepreneur this week, and he said, “This company cut CS by 50% just to see what would happen.”

The same person said, “90% of the companies I talk to are canceling their CSP.”

After a recent merger of two large CSPs, one of their executives posted his resignation on LinkedIn, the TL;DR  was that CS has a lot of promise but executive leadership refuses to give it the budget it needs.

CS is approaching a crisis. The root of the problem is retention, and the belief means that only one group ‘owns’ the number.

Why? No matter how much tech or flesh you throw at a retention problem, CS isn’t going to improve it in any meaningful way…alone.

If your Marketing team targets customers who won’t get value from your product and they buy it, what happens?

If your product is confusing, or buggy, or just sucks, what happens?

If your Sales team sells deals with false promises, what happens?

If your onboarding process stinks, what happens? 

If your Accounting team pisses people off, what happens?

The answers to the above are obvious. What is not obvious? Which of these problems is afflicting your business right now, as you read this, because each of those issues is in a different system, silo and team. 

You aren't paying attention.

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No one owns retention. The obsession with retention has led us to ignore what really matters: what makes customers happy, and what does not.

Today, we have the opportunity to automatically discover almost every issue that detracts from customer satisfaction, route it to the right person, and track its resolution. The Marketing VP targets customers who need the product, the Product Team has a customer-led roadmap, the Billing Team realizes that the auto-renewal process does more harm than good, and the CRO learns which sellers are over and under-selling. 

When was the last time you heard someone say, “We leave no stone unturned in our quest to resolve every customer issue rapidly and intelligently?” 

I have spoken to several executives who say, “I just wouldn’t know what to do with this type of data.” I make a note to never buy their products. They don’t care about customers.

Call me crazy. I want to live in a world where every product or service I buy is awesome. So does everyone else. Focus on being awesome, and you won’t need to worry about retention. 

Let’s try to make it a reality together.

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My neighbor asked me to speak with his son (who is not connected here on LI). The son is a mid-market account manager (post-sales) at a large SI (pure services). His remits are expansion/upsell, renewal assistance, and retention/escalation. His book has 30 customers, and its approximate value is just shy of $1mm annually.

He's stuck.

He's stuck at his company. They pay well. His role isn't challenging him anymore. He doesn't want to do pure sales or pure CS work. He is smart. He is motivated to create a career path. Right now, he can't see the forest from the trees.

After 20 minutes, he asked me what he should start, continue, and stop doing. Great question in this context.

Here was my advice. If you know me well, you know it took many more words than LinkedIn will accept in a single post. 😉

🏅 Start thinking of yourself as a professional athlete.

Professional athletes spend +90% of their time preparing for competition. Prepare like a pro for both internal and external meetings. Study your customers and learn everything you can about them. This will prepare you for your account reviews with your leadership. This will help you blow out your KPIs. This will build the foundation of success. Preparation is hard. It's tedious. You will be working harder than ever. Keep doing it. You will not see results for at least 6 mo. Keep going.  

💡 Continue asking for help.

Tapping into the expertise and experiences of others is a dying art. New people offer new perspectives. Getting advice will help you learn how other pros have built their careers. As an early/mid-career person, building relationships and networks will serve you well now and in the future. You're defined by the company you keep. Expand your community. It will, eventually, unlock opportunities.

🛑 Stop going through the motions.

Lacking purpose, passion, and interest is a career-advancement death sentence. Most importantly, it leads to dissatisfaction, stagnation, and lack of fulfillment in every aspect of your life. Stop just trying to make your numbers. Kill your number. Stop relying on what got you here. Dig deeper to force yourself to grow. Every day can be the first day of school. You have the power to reinvent yourself every day.

You are in the pros now. Be a pro.

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There were 453 jobs posted on Indeed in the US for customer success managers in the past 14 days.

On average, companies interview five candidates before making a hiring decision for a mid-level customer success position. That’s a lot of interviews—and time. With productivity being top of mind for customer leaders, new hires, assuming a good fit, will eventually increase capacity, but the process is a body blow to short-term productivity.

Then there is the risk of a bad hire - the real kidney punch. I won’t go into that in this post.

All this hiring is encouraging, and it also got me thinking about how leaders can directly impact the hiring process without all kinds of process changes and wrangling of resources.

Interviews. Ask better questions. Get better information. Make better hiring decisions.

I’ve hired dozens of post-sales people over the years, and here are six attributes that I consistently interview for.

Technical Preparedness: We sold a solution and are now delivering one. Our people must have the chops/cognition to understand complex platforms, workflows, and ecosystems. Additionally, we have to ensure from the get-go that our associates know how to prepare for a solution-oriented meeting with a customer—substance over fluff.

Attention to Detail: Our teammates must be organized, willing to follow processes, and steadfast in capturing data.

Coachability: Ideal candidates will be open and even excited about learning quickly. We look for people who take direction well. We don’t have a long window for ramp. Humility is key.

Sticktoitiveness: Being on the frontline is arduous. Our associates must be able to manage the emotional peaks and valleys.

Work Ethic: Drive is a key value here. We need people who want to work hard while they’re at work consistently and who take pride in the quality of their output.

Resourcefulness: Our teammates need to be hyper-resourceful, diggers of information, and, most of all, intellectually curious so that they can identify root causes.

Note: I haven’t hired a person in the last 20 years without them taking an assessment designed by Gary Kustis There’s nothing like getting another, unbiased data point with which to make a decision. I'm happy to share how and when I use assessments - just message me.

Also, if you're interested in interviewing like I am, check out what my friends Intertru Inc are doing. Unique and effective.

Otherwise, if you want a copy of our full behavioral interview guide for CS, you can grab it here!

I was speaking to the COO of one of our customers a few weeks back, and he said that Sturdy’s “Response Lag” signal was his “Laptop Smasher.” This signal is defined as a “customer is asking for a status update on an unresolved issue.” If your goal is to make sure your customers feel heard, then it is a bad one.

While not AI-based, the attached regex will help you find some of these messages on your own. If your support or BI system allows you to filter on inbound messages it will provide cleaner results. (There’s quite a bit of contextual difference between a customer asking for an update and one of your people asking a customer for an update).

In most cases, this signal is pretty rare. Typically, it occurs about once per every 1,000 conversations (again, only detecting messages “coming in” from a customer).This signal is important to track for two reasons. The first is that it is almost never self-reported. It is rare for a customer-facing person to say, “Yeah, the customer is upset because I never got back to them.” I am almost certain that you have CS/Support teammates who have a much higher incidence of this signal than your best performers. It also means that you have a grumpy customer that you don’t know about.

The second reason is that Response Lags provide really good data for resolving hidden process or product gaps. If a customer is asking for an update on an issue, it is likely that several other customers are asking about the same thing. Every business is different, but Response Lags will likely indicate that there is a product, process, or person responsible for the plurality of them.

At Sturdy, we use machine learning to track, record, and alert you of Response Lags. We’re also working on some cool stuff that will track the response time of any open issue from any conversation (without requiring a customer to hit the “is this resolved?” button).

How cool would it be to have a dashboard of every “waiting for a response,” email, chat or phone call? We’re working on it.

Give the regex a try, and feel free to DM me with any questions. I hope you don’t smash a laptop. Of course, please regale us in the comments of any learnings you’d like to share on the subject.

Do that hard things,

Steve

At the end of last year, I shared a regular expression (regex) that identifies "contract requests." That's a scary signal for people who like to keep customers.

Today, I want to discuss the scariest of the Scary Six, "Executive Change."

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At my last company, Newton, this signal had the highest correlation to churn and initially resulted in a loss about 50% of the time (for many of Sturdy's customers, this is also true).

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So what is it? Let's say you sell accounting services, and this happens:

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"Hi, I am the new CFO, and I would like a quick rundown of your capabilities."

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The response is often,

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"So nice to meet you! LMK when you have 30 minutes for a quick call!"

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(By the way, usage will be high during this time, and their Health Score will be green.)

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On to the regex…

The first two are specific to HR services/tech, so replace "hr" with "e-commerce," "accounting," "logistics," or whatever business you're in.

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Here's what they do:

1. The first detects when someone says, "Hey, we have a new VP of HR coming on board soon."
2. The second, "I will be taking over the Admin role for this account."
3. The third, "Hey, I wanted to let you know that I will be leaving at the end of January."

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Remember that they return a fair number of false positives (FPs). FPs are not included in the churn rate calculation.

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The frequency of "Executive Change" varies depending on the industry and segment. In the SMB cohort, it occurs in about .1% to .2% of customer conversations. In huge enterprises, around .04%.

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Interestingly, this signal is much more common in the HR space, firing at .3% per conversation.

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There is also a lot of variation in the severity. Still, the correlation to cancellation is the 2nd highest of any signal we currently detect at Sturdy ("I want to cancel" being the highest, obviously). For SMB customers, the churn rate for this signal, if untreated, will approach 70%. It will be lower for enterprise customers.

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Another critical point is that this is a leading indicator. It often occurs long before the cancellation event.

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Why is this signal such a strong indicator? At the beginning of the post, we showed a sample trigger-sequence that ended something like, "Let's do a quick demo!"

What's wrong here? I think it is because one or all of the following is happening:

1. The value of your service can't be communicated in a "quick demo."
2. The new contact has undoubtedly used and trusts a competing solution.
3. The person conducting the demo has not been trained to sell your product, overcome objections, and destroy your competition's product.

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This is a perfect recipe for failure. Here's a scenario...

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Acme Corp sells HR Software on M2M and yearly contracts; it receives:

10k emails and tickets per month (items).
10k items equals to about 2k conversations (1 convo = ~ 4.4 items)
.3% detection per conversation = 6 Exec Changes
Two false-positive (30% FP rate)
50% churn x 4 = 2 losses

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If untreated, Acme loses two customers to this signal per month.

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The good news is that, in my experience, treatment will save about one of these customers each month. How?

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1. Train everyone who touches customers, billing, CS, and marketing to identify the signal.

2. Immediately send the signal to your sales AND marketing teams.
Someone should attempt to discover the product the new contact used at their former company.

3. A salesperson must schedule a demo as soon as possible. (At Newton, our KPI was to conduct the demo within ten days). The seller should come armed with useful information, like usage data candidates hired (e.g.), and be prepared to sell against the new contact's previous solution.

4. In parallel, the marketing team checks LinkedIn to see if the previous contact has landed a new job. If not, someone should reach out and see if they need help in their job search (after all, you sell to companies that hire these people). If the person has landed somewhere, send them a note, a gift basket, or whatever you think is appropriate.

5. Send the previous contact to the Sales team as an SQL.

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(Shameless plug: Sturdy has AI-language models that find 1, automatically route 2, and can tell you if 3 and 4 happened.)

The result of this process is a successful "double-dip". You may save a customer and gain a lead for your sales team. Ironically, if your competition is not tracking the Executive Change signal, your chance of closing that deal is very high.

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The second line of that image is a regular expression (aka regex). If your support or ticket system supports regex, try that search against the content of your tickets. You can probably hand this to your BI team, too. It will find customer comments like, “Hey, we’re just cleaning up some files, and can we get a copy of our agreement?”

For some background, at my last company, I had a standing meeting on my calendar every week to read random support tickets. From this, the concept of the “Scary Six” was born.

One of the Scary Six was a “Contract Request.”

At Newton, about 70% of the time, when a customer requested a copy of their contract, it was a risk to their revenue longevity. We audited them regularly and found they broke down into the following buckets:

We want to know when we can or how easy it is to cancel (50%).

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We just need our contract because we lost it (30%).

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We are getting bought, going out of business, etc. (10%).

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We need to see if we can cut some costs (10%).

We saw this flag about once per 6,000 email conversations (.0167%). Generally, this average rings true for most businesses we work with today.

Combining these two metrics, we estimated that for every 10,000 email conversations, we received about 2 Contract Requests. In other words, for every 10,000 emails, we had 1.4 customers at risk.

Once we identified Contract Request as a revenue impact, our incredible CS team trained everyone to identify “Contract Request” language. We then built a process for addressing them.

The before/after impact of identification and triage was remarkable and resulted in doubling the retention rate for this signal.

Over the next few weeks, I will post the rest of the “Scary Six” with their regex. Those left on the list are “Executive Change,” “Renewal,” “Response Lag,” “Overpromised,” and obviously, “Cancellation.”

Please let me know if you have any other “Scary” triggers. I hope you give this a shot and find it illuminating.

When I started my first SaaS company, I had a standing meeting on my calendars every week to read random support tickets (random is the crucial word, by the way). Reading tickets was always illuminating and often painful. One of our learnings was a churn risk called "New Sheriff."

First, don't get me wrong, we trusted our team. But if there's one thing that always bothered me, I never really knew what our customers said about us. And, for that matter, what we were saying to them. 

Eventually, we built a suite of search strings, and if you want to try some yourself, here are a few simple ones:

We would search for product issues with things like: "doesn't work"; "confusing"; "annoying" bug, and "clear cache."

Searching for things like "gotten back to me" and "still waiting" would indicate that our customer was still awaiting a response. I would look for revenue issues with: "new VP,"; "new vice president,"; "new manager,"; "has left the company,"; "copy of our contract,"; "renewal date," and "overdue."

You are probably thinking, "Why would I look for "new VP" or "new manager"?" It comes up like this, "Our HR Manager has left the company recently, and I need a login for our VP of HR, Jim Smith."

At Newton, HR executives were responsible for hiring/firing HR software decisions. We sold HR software.

A new HR executive was the highest indicator of churn in our business. By that, I mean, left unattended, our customer was almost certainly (80%+) going to churn at renewal. From this, the term "New Sheriff" was coined. A "New Sheriff" customer was no longer forecasted to be a long-term customer and thus needed to be resold. 

We trained everyone at Newton on identifying a "New Sheriff" and where to send the alert - manually.

When we got a "New Sheriff" alert, several people got to work. The CS team would pull usage data and some other vital metrics. The account management team would reach out to identify the new VP and schedule a demo of our solution.

Our sales leadership would also reach out to the former executive. We'd offer to help them network to find a new job or make inroads at their new company. 

In doing this, we turned our "churniest" event, one with an 80% churn rate, to one with a 30% churn rate (from -.8 to -.3). We also gained a lead for our sales team that closed 80% of the time (from 0 to +.8). In other words, we turned a very churny event into one that gained a half a customer. 

If you'd like to capture "New Sheriffs," give me a shout, and I'll send you a few more advanced search strings. (If you’re a Sturdy customer, our models auto-flag this as “Executive Change.”)- Steve@sturdy.ai

About the Author

Hi, I'm Steve Hazelton. I am one of the founders of a startup that helps businesses better understand their customers by using AI to identify risks and opportunities inside the unstructured data trapped in emails, chats, and phone calls. I received help writing this article by using generative AI for data collection (if you don’t know what that means, you are not alone, go here).

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Caveat Emptor: Since I am the founder of a startup that relies heavily on AI to find happy and unhappy customers, I certainly have "bought in" to using AI for business to leverage untapped data streams. So, consider yourself warned. I also started my career a long time ago as a recruiter for technology companies, later built and sold an HR tech company, and later started an AI company…so the intersection of AI and career advice collide here.

Introduction

Often lost in this discussion of AI technology is the discussion of its impacts on our teammates and coworkers. What does the widespread adoption of AI in business mean for the careers of people who work at these businesses? While many of us are, and should be, concerned about job destruction, I want to talk about job creation. 

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Note: There is, at present, endless discussion on AI technology, which I won't bore you with here. If you want to learn about the different types of AI and AI tools, like Generative AI, Synth AI, Machine Learning, and others, you can read an article our co-founder wrote here.

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From a career standpoint, the most significant change our businesses will see this decade is creating a new, high-paying job in AI Operations. This article will help you, the reader, define this role when you decide to hire this person, or if you desire to be that person, how to create the role in your company.

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This person will leverage AI tools and products to improve a business's top and bottom-line revenue. They'll find revenue opportunities, prevent cancellations or churn, and make people more efficient. They will be indispensable. 

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Dan Corbin, an instructor at the Pragmatic Institute, states, "If you can change your mindset as a company and understand the capabilities of AI, this is where AI operations come in. You need this AI Operations Director to ask, "How do we tackle this from a macro level?" You must think about AI from an organizational perspective to leverage it to its full capacity."

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We are at the beginning of a major, major shift in employment. Fifty years ago, did companies have an IT Manager? No, but they do now. Thirty years ago, did they have an e-commerce manager? Again, no, but they do now. Ten years from now, will companies have someone in charge of AI operations? Of course, they will.

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Adoption is inevitable because the gains are too significant. As Mike Evans, Director of Customer Care and Analytics at Laerdal Medical, states, "You need someone to own this." Companies like MassPay have already implemented such a role, which they attribute to the 100% customer retention of their "Top 100" last year. Hawke Media, the top performance marketing agency in the country, shifted the purview of their existing Director of Business Intelligence to include AI Ops. They then improved revenue retention by 30% MoM in less than six weeks.

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With that out of the way, let's get started.

The Role of a Director of AI Operations

As businesses continue to embrace AI technologies, the need for dedicated professionals to implement and oversee these systems will become critical. Enter the Director of AI Operations.

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At its core, this role is creative: you need to think of new ways to solve old problems in ways that have never been done before. "How can we use tomorrow's tools to solve our problems today?" This is a key point. AI will help you solve problems in ways you never considered before because they were previously impossible.

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The Director of AI Operations will be the key player responsible for developing, implementing, and managing AI strategies.

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The AI Business Director should be able to create and implement an AI strategy that answers the following:

How can we use AI to find revenue opportunities?”

How can we use AI to identify and reduce revenue risks?”

How can AI make our teammates more efficient?”

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This multifaceted role requires a deep understanding of AI vendors, data privacy, and a visionary mindset to leverage AI's potential effectively. 

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Note: This job does not require coding. This person isn't building AI; they are identifying the areas where AI can improve business performance.

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Let's take a closer look at the responsibilities of this job:

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Strategy Development: "What problems are we trying to solve?" The Director of AI Operations collaborates with various departments to identify areas where AI can be integrated to capture risk and opportunities or to improve efficiency. They create a comprehensive AI strategy aligned with the business's overall objectives. 

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Data Discovery: "What data could AI illuminate that we've previously been unable to use?" For example, a Director of AI Operations could use emails to create a new data stream that correlates customer product confusion with unhappiness and eventual cancellation. 

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Data Management: "What are the security, privacy, and regulatory challenges with our approach." AI heavily relies on quality data to make accurate predictions and decisions. The Director ensures that data is collected, cleaned, and stored securely. This person should be able to deep-dive on a vendor's privacy and regulatory compliance.

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Implementation: "What systems will we need to leverage, and how will we accomplish this?" Once the AI strategy is in place, the Director oversees the implementation of AI projects, ensuring seamless integration with existing systems and addressing any technical challenges that arise. Just as important, this person will need to drive the "people-side" integrations and help people leverage these new data streams. 

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Performance Monitoring: "What are the success criteria?" Monitoring the performance of AI systems is critical. The Director tracks key performance indicators to measure the impact of AI applications and makes adjustments as needed. Critical here is to answer, "Is this driving the desired outcomes?"

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Ethical Considerations: "Should we even use AI for this?" Some AI systems handle sensitive data and will replicate previous biases. A crucial question is, "Is the AI making decisions?" If "yes," then much thought should be put into whether or not AI is appropriate.

Growth of AI Jobs

According to a study conducted by the World Economic Forum, AI is estimated to create 58 million new jobs by 2024. This includes a wide range of roles, from data scientists and AI engineers to, of course, Directors of AI Operations. According to HireEZ, one of the world's largest outbound recruiting platforms, demand for AI-related positions has risen by 60% since 2021.

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On the flip side of that coin, higher education and e-learning platforms are seeing a surge in interest in AI courses. Pablo Garcia, Content Lead at CXL, the top marketing e-learning platform, states, "CXL saw a much higher interest in AI courses among our students in 2023, with a 785% increase in engagement for the Advanced AI in Marketing course."

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As more companies recognize the potential of AI and seek to stay competitive in the market, the demand for AI professionals is set to skyrocket. A survey conducted by Deloitte further reinforces the growing importance of AI in businesses. It revealed that around 61% of surveyed companies have already implemented some form of AI into their operations. That means 61% of respondents are looking to hire a Director of AI Operations if they haven't already. Don't get left behind. 

Where to Start

If becoming your company's Director of AI Operations seems daunting, don't be discouraged if you don't have experience. Very, very few people do. Get started now, and you'll be far ahead of everyone else.

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Where would I start? I would look at my current role and think, "How could AI help my current company keep customers longer? Or, how could AI make my group more efficient?"

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What problems are we trying to solve?”

What data could AI illuminate that we’ve previously been unable to use?”

What are the security, privacy, and regulatory challenges with our approach?”

What systems will we need to leverage, and how will we accomplish this?” 

What are the success criteria?”

Should we even use AI for this?” 

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Get on it! Also, if you’d like more help, download our AI Retention Plan & Calculator.

Are you looking to hire someone to manage AI Operations?

While writing this article, a friend sent me a job description for a “Head of AI Product Management” at a significant online streaming company. It pays 900k/year. Hmmm… A recent Sturdy poll on LinkedIn concluded that over 50% of respondents already have someone in an AI Operations position, with 25% looking to fill the role in 2024. If you’re interested in hiring for a Director of AI Operations, feel free to copy and paste the job description below:

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Director of AI Operations

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Role Overview: The Director of AI Operations will be the key player responsible for developing, implementing, and managing AI strategies. This multifaceted role requires a deep understanding of AI vendors, data privacy, and a visionary mindset to leverage AI's potential effectively.

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Key Responsibilities:

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• The Director of AI Operations collaborates with various departments to identify areas where AI can be integrated to capture risk and opportunities or to improve efficiency. They create a comprehensive AI strategy aligned with the business's overall objectives. 
• Identify areas of improvement throughout the business and implement AI workflows.
• The Director ensures that data is collected, cleaned, and stored securely. This person should be able to deep-dive on a vendor's privacy and regulatory compliance.
• The Director tracks key performance indicators to measure the impact of AI applications and makes adjustments as needed.
• Once the AI strategy is in place, the Director oversees the implementation of AI projects, ensuring seamless integration with existing systems and addressing any technical challenges that arise.
• Stay updated with the latest generative and synthesis AI trends and technologies to ensure the company stays ahead of the curve.
• Develop AI strategy and roadmap and act as the foremost thought leader on ethical considerations such as how AI systems handle sensitive data.
• Train colleagues and other teams on AI workflows and best practices for their departments.

Requirements:

• Bachelor’s degree in Computer Science, Data Science, or a related field. Master’s degree preferred.
• Proven experience identifying areas where AI can improve business performance and executing those strategies.
• Strong analytical and problem-solving skills.
• Ability to work collaboratively with diverse teams.
• Excellent communication skills, both written and verbal.

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As always, thanks for reading. Feel free to reach out to us to talk further.

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Steve

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Customer churn is a term often used in the SaaS world, but what does it actually mean?

Simply put, churn is the rate at which customers are lost. These are customers that have canceled your service and aren’t coming back. It can be calculated for individual customers (B2C) or for an entire company (B2B). Four different types of churn are commonly measured: customer churn, revenue churn, gross churn rate, and net churn rate. Let's take a closer look at each type.

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Customer Churn

Customer churn is the most commonly used type of churn. It is the percentage of customers that stopped using your company's products or services during a specific time frame. You can calculate your customer churn rate by dividing the number of customers you lost during that period — say a quarter — by the number of customers you had at the beginning of that period. 

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Let’s pretend for a moment that you work on the growth team at SaaS.io, a new (you guessed it) SaaS startup. Over the last few months, SaaS.io has continued to grow hand over fist with little to no customer churn. However, customer acquisition has begun to slow, and your boss is asking you to calculate the customer churn rate in October. This equation is relatively straightforward. At the beginning of October, Saas.io had 54 customers. However, by the end of the month, two had churned. That means your customer churn rate in the month of October was 3.7%.

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1. Total customers at the beginning of a period: 54

2. Number of customers lost in period: 2

3. Customer Churn Rate = (2/54)*100 = 3.7% (that is a great number, by the way)

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Revenue Churn

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Revenue churn is similar to customer churn, but instead of measuring customers leaving the company, it measures the amount of revenue lost due to customers who have left or downgraded their plans. To calculate revenue churn, divide the total amount of revenue lost in a certain period by the total revenue at the beginning of that period. 

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If we head back to our SaaS.io example, it’s important to note that the October revenue churn is much scarier than the customer churn. Yes, only two customers churned, meaning there was a 3.7% customer churn rate. However, one of those customers (Customer 2) accounted for 11% of MRR (monthly recurring revenue). Customer 1 generated only $6,000 in MRR, whereas Customer 2 generated $22,000 MRR. That means that at the beginning of October, SaaS.io’s MRR was $200,000. By the end of October, the revenue churn was .14.

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1. Total revenue at the beginning of a period: $200,000

2. Net revenue lost in period: $6,000 + $22,000 = $28,000

3. Revenue Churn Rate = $28,000/$200,000 = .14

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Gross Churn Rate

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The Gross churn rate takes into account both customer and revenue churn. It measures the total number of customers and revenue lost in a certain period, divided by the total number of customers and revenue at the beginning. This gives an overall picture of how much business is lost in a given time frame.

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If we apply this to SaaS.io, the MRR for October was $200,000, and users canceled $28,000 worth of contracts. That means the gross churn rate will be 14%

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1. Total revenue at the beginning of a period: $200,000

2. Net revenue lost in period: $6,000 + $22,000 = $28,000

3. Gross Churn Rate = ($28,000/$200,000) x 100% = 14%

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Net Churn Rate

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Net churn rate considers both customer and revenue churn. However, it also includes new customers and expansion revenue acquired in a certain period. Expansion revenue is the additional revenue you generate from existing customers through upsells, cross-sells, or add-ons. That’s why net revenue churn gives an overall picture of how much business is being gained or lost in a given time frame. 

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A month has passed since those two customers, and 14% of gross MRR was lost. Saas.io is currently at $172,000 MRR in November, as no additional sales have been made. Unfortunately, November has also seen $12,000 in contract losses. Luckily for Saas.io, a few existing customers have upgraded their plans, generating an additional $10,000 in revenue. Your boss asks you what the net churn rate for November is. First, you must subtract the customer upgrade revenue from the revenue lost in downgrades and cancellations. Then, divide that number by the revenue at the beginning of November.

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1. Total revenue at the beginning of a period: $172,000

2. Net revenue lost in period: $12,000 - $10,000 = $2,000

3. Net Churn Rate = $2,000/$172,000 = 1.1%

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Leaky Bucket Equation

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At the beginning of this post, we noted that four types of churn could be measured. That isn’t entirely true, so here’s a bit of a bonus round. SaaS angel investor, Dave Kellogg argues that the leaky bucket equation “should always be the first four lines of any SaaS company’s financial statements.” Kellogg continues, “I conceptualize SaaS companies as leaky buckets full of annual recurring revenue (ARR). Every time period, the sales organization pours more ARR into the bucket, and the customer success (CS) organization tries to prevent water from leaking out”.

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Kellogg defines the leaky bucket equation as “Starting ARR + new ARR - churn ARR = ending ARR”.

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If we apply this to our Saas.io example, we can determine that the starting ARR in the fourth quarter (Q4) of 2022 was roughly $400,000. The new ARR in Q4 ‘22 was $56,000, and the Churn ARR in that same time period was $45,000. In other words:

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1. Total starting ARR: $400,000

2. New ARR: $56,000 & Churn ARR: $45,000

3. Ending ARR = $400,000 + $54,000 - $45,000 = $409,000

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Churn is an important metric to track for any SaaS company, as it can be used to identify trends, measure loyalty, and assess the effectiveness of customer retention strategies. Calculating churn rates can help companies identify which customers are more likely to leave and which types of customers are the most valuable. By understanding churn, businesses can take steps to improve customer retention and keep their business running smoothly.

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We’ve learned from our own operational experiences as entrepreneurs, product owners, revenue leaders, advisors, and board members that for SaaS companies to remain competitive, customers must be at the center of product and go-to-market decisions. Every team needs to optimize to create end-user value. To understand what really creates end-user value, you have to listen to customers — closely. Unfortunately, listening to customers is harder than it sounds.  

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The volume and velocity of data that businesses are generating is mindblowing. Ironically, most of this data is unstructured and trapped in emails, support tickets, Slack, call transcripts, user communities, and survey results. Most product teams struggle to grasp this valuable “dark data” at scale. Some are starting to experiment with AI to make sense of user interactions. This has introduced two new big problems: getting data out of all these sources and the murkiness of the data itself. This is why we create Sturdy PX for product-oriented teams. 

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My guess is that forward-looking teams trying to use AI are running into the same big issues that we had to solve to get Sturdy in the wild — mainly data transformation. We’ve seen this called data munging. Yuck. Whatever you call it, data prep tasks are time-consuming and tedious. We’ve learned that teams spend an enormous amount of time on manual data wrangling, which slows access to the valuable stuff -  the analysis and, ultimately, the insights!  About 70% of our tech is dedicated to automating data preparation tasks. For example, most companies collect data in inconsistent data formats across multiple modes/data sources. Sturdy standardizes the data formats, cleans the data, and synthesizes it with structured data like CRM data.

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Leveraging AI, we're transforming traditional product research by empowering anyone on a product team with self-service access to the unsolicited, unbiased, and unabridged voice of the user. Sure, surveys, prototype tests, focus groups, etc., are still in the mix. They should be. But we’ve found that getting a consistent stream of accurate, categorized, product and user-centric insights really scratches that itch that we’ve had as product people. 

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For a limited time, we’re offering Sturdy PX to qualified product teams for free. It’s easy to get started, and the not-free version is pretty darn affordable, even on a tight budget. Get Sturdy here.

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Introduction:

In today's digital age, the sheer volume of data generated by businesses is staggering. Ironically, most of this data is unstructured and trapped in things like emails, support tickets, and phone calls. Until now, this meant that the only way to extract valuable insights was by using manual labor to categorize them. 

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This is where the power of Artificial Intelligence (AI) comes into play. By harnessing AI, business leaders can unlock the hidden potential of unstructured data and gain a competitive edge. In this blog post, we will explore how business leaders can effectively leverage AI to extract valuable insights from unstructured data and drive innovation.

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Understanding Unstructured Data:

Unstructured data refers to any information that lacks a predefined data model or organization. It includes text documents, social media posts, images, audio, videos, and more. Unstructured data is generated in abundance from various sources such as customer feedback, emails, surveys, social media platforms, and help desk interactions. The true value of unstructured data lies in its ability to reveal patterns, sentiments, and trends that can shape business strategies.

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AI and Unstructured Data: Extract, Diagnose, Proact:

Artificial Intelligence, particularly techniques such as natural language processing (NLP) and deep learning, can process and analyze unstructured data with remarkable accuracy. By utilizing AI, business leaders can transform this seemingly chaotic mass of unstructured data into actionable insights.

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Information Extraction:

1. First, AI removes the ”manual labor tax” associated with leveraging unstructured data. AI efficiently extracts relevant information from unstructured text data, at scale, for a fraction of the cost of manual processing. Text mining techniques, including entity recognition, sentiment analysis, keyword extraction, and topic modeling, can be used to identify critical insights buried within vast amounts of unstructured text. This information can be invaluable for market research, competitive analysis, and trend forecasting.

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Knowledge Diagnostics:

2. The next step after extraction is leveraging this data to diagnose risks and opportunities. AI converts unstructured data into a powerful diagnostic tool. Unstructured data sources like customer emails and chat transcripts contain valuable information about individual products and processes. For example, a business leader may realize that just one feature is causing the majority of customer unhappiness. They might realize that a certain Account Representative is very good at improving sentiment. The possibilities for improving our businesses are almost endless.

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Proactivity and Prediction:

3. The “holy grail” of unstructured data is leveraging this information and knowledge to proact on and predict future events. By analyzing historical unstructured data, leaders can identify issues and monitor them going forward. For example, data might reveal that customers are more likely to cancel within 6 months of having a leadership change event. Not only will AI help leaders identify this warning sign, but by analyzing unstructured data in real-time, it will warn leaders and provide them with the opportunity to save revenue.

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In the era of big data, unstructured data holds immense untapped potential. Business leaders who harness the power of AI can gain a competitive advantage by extracting valuable insights from this wealth of unstructured information. From sentiment analysis and text mining to predictive analytics, AI techniques provide the means to unlock the hidden value within unstructured data. By embracing AI and leveraging unstructured data, business leaders can make more informed decisions, drive innovation, and stay ahead in an increasingly data-driven world.

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Product management has evolved significantly over the years, adapting to customer expectations, increased competition, and technological advancements. What was once a fragmented and operational role is now one of the most strategic and cross-functional disciplines in many organizations. Modern product management has two key goals; delivering customer value and, subsequently, driving business growth.

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In the past, product managers were often hardened SMEs relying on previous experiences and assumptions. However, in recent years, subject matter expertise has become less critical as the focus has shifted to better understanding a wide variety of users. Product managers now emphasize understanding customer needs, conducting user research, and gathering feedback to inform feature prioritization and, more importantly, the desired value-driven outcomes for customers. 

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These days, PMs have a massive amount of data at their disposal, enabling experimentation and validation. This iterative mindset helps mitigate risks, validate hypotheses, and optimize product features based on user feedback. And, to that end, user feedback is the key — the currency of modern product management feedback is what makes or breaks a product. 

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Traditional user feedback methods are a miss

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Traditionally, user feedback has been gathered through surveys and customer interviews. Surveys are emailed to customers to gather feedback. Pretty straightforward. Customer interviews allow PMs to dig deeper into customer motivations, pain points, and specific use cases.  

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What do traditional product research methods have in common? They are labor-intensive, often expensive, and time-consuming, requiring reliance on other teams to complete. And, maybe the biggest drawback is that the data and insights generated are typically from a small subset of the product’s user base. 

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Welcome to the AI-era

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AI-powered platforms are making it possible to sift through data using natural language processing (NLP) and machine learning algorithms to quickly analyze large amounts of customer-generated information like email, tickets, call transcripts, and more. These data sets have been nearly impossible to access in the past because of their unstructured nature. AI-based tools can search for patterns and recognize key signals that might be difficult and even impossible for humans to spot, especially at scale. 

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AI is catapulting PM teams into a new era by enabling them to quickly and accurately identify trends in user preferences and behaviors related to specific feature requests, common product defects, and areas the most prone to user confusion. 

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Additionally, AI-based platforms analyze vast amounts of data in real-time, helping product managers iterate and experiment faster while reducing costs and reliance on other teams. With the help of AI, teams can gain valuable, unbiased insights into their products more efficiently and more effectively than ever before.

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Real use case examples are maturing 

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For example, product teams at HireEZ, the award-winning outbound recruiting solution, use Sturdy to slice and dice real user feedback like feature requests, confusion, and product friction by product mix, segments, and cohorts to understand better how to maintain product-market fit across their customer base. 

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Teams at MP, a leading provider of innovative HR technology and managed HR services, no longer rely solely on surveys and interviews to understand customers better. They use the real voice of the customer from email and support tickets to better identify opportunities to improve their offerings. 

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It’s no surprise that AI is transforming product management. The function was poised for evolution. AI is now simply accelerating more teams to become even more customer-focused, data-driven, collaborative, and iterative. Product managers are embracing digital transformation, agile methodologies, and a customer-centric approach to navigate complex market dynamics and deliver innovative products.

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In today's world, the power of AI is undeniable and, in many cases, is yet unknown. Businesses are leveraging this technology to increase their productivity and efficiency in ways that were never before possible. From semantic search to content generation, AI has enabled teams across the globe to work smarter, faster, and more effectively than ever before. But what really takes AI-powered productivity to the next level is converging information. Zeya Yan and Kristina Shen contend,

To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information…While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.”

In other words, this synthesis or convergence of information promises to revolutionize how businesses operate. In this blog post, we'll explore how much time teams are currently wasting with mundane tasks and how Synthesis AI can ameliorate that. 

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Before we do that, let’s first define what Synthesis or Applied AI is. In simple terms, it is the use of artificial intelligence (AI) in a practical context. It involves using AI algorithms and techniques to solve real-world problems or create new products or services by synthesizing vast volumes of data into insights. Examples include self-driving cars, facial recognition software, natural language processing, and machine learning. Applied AI can help businesses save time and money by automating routine tasks and providing better insights into customer behavior. For example, Sturdy is an Applied AI vendor using several AI techniques to help businesses understand and act on customer and prospect interactions more effectively.

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Let’s take a deeper look at how Applied AI can power the productivity of your team. We’ll begin by taking a close look at the standard American work week. According to a 2023 Zippia study, the average American adult works 38.7 hours weekly. That is roughly 8 hours a day during the standard work week. There seems to be plenty of time to accomplish meaningful work, but there’s a catch. Workers spend more than half of their workday searching for information and doing manual data entry. A 2022 Coveo report found that “the average employee spends 3.6 hours daily searching for information.” The report highlights an increase in one hour per day over the last year, a “trend heading in the wrong direction.” Imagine the impact that has on your business. To keep the math simple, if you have 100 employees working 262 days a year, that’s nearly 100,000 hours of wasted time a year. And as the old adage goes, time is money. 

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But it doesn’t end there. Your employees spend nearly as much time simply moving data from one place to another as they are searching for information. A 2021 Zapier study concluded,

76% of respondents said they spend 1-3 hours a day simply moving data from one place to another. Additionally, 73% of workers spend 1-3 hours just trying to find information or a particular document.”

Put that into perspective for one moment. That means your workforce is potentially spending up to 75% of their time looking for information or moving data from one location to another. Thanks to AI, it doesn’t need to be this way.

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For example, Sturdy uses Applied AI to make data entry and searching for information unnecessary. Sturdy collects all unstructured interactions with your prospects and customers—the stuff stuck in various silos—emails, chats, tickets, call transcripts, etc. It cleans it up, synthesizes it with different data sources, and gets it into one searchable system every team will use. And let’s be honest, your business will never generate less data than it does now. With Sturdy, the dark data trapped in your business finds the people, systems, and teams that need it most without requiring data entry. Now organizations can route cancellation insights to account managers—surface unbiased feature requests to product teams—send bug reports to the engineering team, and more. Sturdy automatically delivers the insights to answer the “why?” and “what next?” to the teams and systems that need it most. Yesterday you searched for information. With Sturdy, it will find you. 

What is AI for business?

AI is not a fad. It’s the news. It dominates the talk of every business conference. And it is the number 1 topic of countless leadership meetings. CEOs are challenging their teams to leverage AI in every facet of their businesses to increase their productivity. gain deeper insights into user behavior, automate mundane tasks, and drive deeper insights with which to make critical decisions. It’s here. The big question is: How can you use it in your business right now?

GPT has catapulted AI into the spotlight, but does it translate into measurable productivity and revenue gains? A recent study by the  National Bureau of Economic Research suggests that even in these early innings of AI for business, it’s moving the needle for early adopters.The study’s authors, Erik Brynjolfsson, Danielle Li Lindsey, and R. Raymond, concluded that AI “disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve.” Our takeaway from the study is that AI is already helping humans do things faster with often better results.

AI provides businesses with numerous automation opportunities, which can help save time and money while increasing accuracy at the same time. Automation technologies such as natural language processing allow machines to interpret human speech or text input without any manual intervention required from humans. This eliminates tedious data entry tasks from employees’ workflows so they can focus their energy on higher-value activities. According to a recent Zapier study, “76% of respondents said they spend 1 - 3 hours a day simply moving data from one place to another. Additionally, 73% of workers spend 1 - 3 hours trying to find information or a particular document.” Additionally, automated processes reduce errors due to human factors like fatigue or distraction, which could otherwise lead to costly mistakes. The same Zapier study confirms this, arguing that “83% of workers said they spend 1 - 3 hours a day fixing errors.” That’s a full workday moving data around, looking for information, and fixing human errors. Imagine what you or your team could do with their day if that were a thing of the past.

The fact is that your company will never generate less data than it does now. The issue is that traditionally, 90% of data generated and collected by businesses is dark—untapped, and often completely unknown. Thanks to applied AI, companies can now use advanced algorithms to easily analyze large unstructured data sets, allowing them to understand consumer and customer behaviors better. This application spans the organization from marketing to engineering, product to customer experience, business intelligence to RevOps, etc. Imagine if these teams had their very own AI teammate that organized every interaction with your prospects and customers and turned it into knowledge and insights to help make everyone’s job easier. Now that’s possible with AI. 

Harness the power of AI today. Simple, smart, and safe.

On a more grounded note, AI will have growing pains along the way. It has problems of its own, especially for businesses that need to leverage it to stay competitive. 

It’s clear that adopting AI is no longer a question of “if” but “when.” It’s an opportunity facing every leader in every industry. And as we confirmed in our research for this report, there are huge incentives to move quickly. But before you leap into AI-powered solutions for your business, you must evaluate them properly—not only from a technical standpoint but also from a functional perspective.

Let’s be honest, millions of dollars will be wasted trying to “roll your own” AI. Before attempting a bespoke AI project, it is important to understand the challenges.

Cross-modal data integration

Cross-modal data integration refers to the process of combining and analyzing data from different modalities or sources, such as email, voice, chat, CRM data, ticketing data, and more. This is one of the biggest blockers for teams trying to build their own AI solutions. Cross-modal data integration aims to extract meaningful insights and knowledge from diverse data sources that may provide complementary or redundant information.

Cross-modal data integration involves several steps, including data preprocessing, feature extraction, alignment, and fusion, or what we call data joining. During data preprocessing, the data from each modality is cleaned, standardized, and prepared for the AI to analyze. Feature extraction involves extracting relevant features or characteristics from each modality, such as text features. Alignment involves mapping the features from each modality onto a common feature space, which enables them to be compared and combined. Finally, fusion involves combining the features from each modality to generate a unified representation of the data. 

​​Cross-modal data integration is integral for AI for Business. For example, when you want to know the most commonly requested feature for a specific cohort of your customers, you need to combine data from multiple inboxes, systems, your CRM, and other modalities to provide a comprehensive and accurate answer.

Data cleansing and normalization

Data cleansing and normalization are critical steps in preparing data for AI applications. In simple terms—garbage in, garbage out. They are also insanely laborious. AI algorithms rely on accurate and reliable data to make predictions or generate insights. Data cleansing and normalization are critical to ensure the data used to train AI models is accurate, consistent, and complete. Think of it this way, you can improve the performance of AI algorithms by reducing noise like duplicate data and other inconsistencies. This can help AI models to make more accurate and reliable predictions. And cleaning and re-structuring data helps to make AI models more interpretable by reducing the complexity and variability of the data. This can help identify the most important features or variables driving the predictions or insights generated by AI models.

One of the most common reasons AI projects fail is due to data cleaning and normalization or, rather, the lack thereof. Business data can come in different formats, such as email, tickets, call transcripts, and more, each with unique characteristics and challenges. Normalizing and cleansing data across these different modalities is a complex task. Now consider that the amount of business data available for analysis is growing rapidly, making it difficult to manage and process data efficiently. This can be particularly challenging when cleansing and normalizing data, which can be time-consuming and computationally intensive.

To compound matters even more, business data is always stored in different systems or databases, which are infrequently compatible with each other. Just think how many different people are emailing your customers. Daunting.

Data cleansing and normalization are important but challenging steps in preparing data for AI applications. It requires domain expertise, technical skills, and a deep understanding of the data and the business problem.

User interface

Let’s say you can get clean, structured data into an AI or large language model. Now what? Now you need a user interface (UI), so business people can inform decisions and workflows with AI-generated insights. This isn’t trivial. You’ll need another team of developers because the people cleaning and preparing the data aren’t the same people who will build an end-user UI. Now you need to build some more software.

A well-designed UI helps users understand an AI system’s capabilities and benefits and increase their willingness to adopt and use it. The UI communicates the outputs and insights generated by the AI system in a way that is easy to understand and interpret. The UI can provide a way for users to input data, provide feedback, or customize the AI system to their needs. This can help improve the AI system’s accuracy and relevance and increase user value.

Data permissions

What next? Sigh. Don’t forget about data permissions. You are going to need them.

Data permissions refer to the rights and permissions required to access and use the outputs from your AI project. They are a critical component of data governance and must ensure that data is accessed per your organization’s policies. Think of this as who can see and use what.

Depending on the nature of the business and the data being used in the AI project, there will likely be requirements around data access and use. Ensuring that the AI project has appropriate data permissions can help to ensure compliance with relevant laws and regulations, such as data protection laws like GDPR.

Simply put, you must build more software to manage who can see what. Data permissions are often reviewed and updated to ensure ongoing compliance with and changing business needs, so your permissioning software has to be commercial grade.

Automations and exhaust

Insights from operational AI systems involve integrating AI models and insights into existing business processes and systems. You must get AI-generated insights to the humans and systems needing the knowledge. This may involve building APIs that allow the model to be called from other applications or systems or integrating the model into your teams’ existing tools like your CRM, email, etc.

Once the AI model is integrated, you have to automate the generation of insights. This may involve setting up automated alerts or reports triggered based on specific events or data conditions or integrating the insights into a dashboard that provides real-time insights (data UI).

By automating insights from AI systems, businesses can gain real-time, actionable insights that can inform decision-making and drive business outcomes. Automating insights can also help improve businesses’ process efficiency and effectiveness by reducing the need for manual analysis and decision-making. Vernon Howard, Co-Founder & CEO at Hallo, exclaims that with AI,

I can automate 20 to 30% of my work now.”

The takeaway is that if you can generate insights, you must autonomously get them to the right place.

Integration requirements

One of the main failure points for AI-related projects is unsustainable methods of extracting and converting data. In the past, this was done manually by interns, business analysts, and data engineers. Technological advancements like APIs make modern data capture processes instantaneously and consistently. This frees data and BI professionals from arduous entry work, focusing their efforts on more rewarding, core business responsibilities.

When selecting any technology solution, it’s important to ensure that it has integration APIs (Application Programming Interfaces) to enable seamless integration with your other systems and applications. Integration APIs allow different software applications to communicate with each other, exchange data, and perform tasks without the need for manual intervention.

Ideally, look for solutions that build direct integrations to platforms instead of using third-party integration platforms. Third-party platforms offer a fast way to connect systems but often lack configurability and data classification controls. Also adding a third-party data integration solution can also introduce another data processor to consider.

Data privacy

The subject matter experts we interviewed for this report agree that privacy concerns are the number one reason AI initiatives fail to launch. Xiaoze Jin, the Lead AI/ML Solution Architect at Rackspace Technology, states,

We’re in a very early inning of cloud AI as a SaaS offering. Privacy, above all else, is most important. Beyond privacy lies responsible AI/ethics, federated learning, and zero-trust framework security.”

Due to privacy concerns and stringent compliance regulations, functional leaders are often stymied by infosec and privacy teams reluctant to allow access to collect or process user data, preventing them from taking full advantage of AI-driven insights. Yacov Salomon, Founder & Chief Innovation Officer at Ketch, states,

Be aware of privacy, the ethical use of data, and the governance of data.”

He continues,

...the world is evolving, and if ML and AI are involved, you need to scrutinize. Governing around data makes a big difference.”

Data privacy laws are designed to protect individuals’ privacy and personally identifiable information (PII). PII refers to any data or information that can be used to identify a specific individual, directly or indirectly. PII can include any information that can be used to identify an individual, such as their name, email address, social security number, phone number, home address, date of birth, driver’s license number, passport number, biometric data, or any other unique identifier.

Any commercial AI solution should have a detailed and thorough approach to privacy. Ask for a copy. Otherwise, look for solutions that automatically de-identify data. The de-identification process can involve removing or masking certain data elements, such as names or addresses. Of course, there are different levels of de-identification, ranging from removing obvious identifiers to more advanced techniques that involve complex algorithms and statistical analysis. The effectiveness of de-identification methods depends on the type of data being processed and the acceptable re-identification risk threshold. Patricia Thaine, Co-Founder & CEO at Private AI, argues,

The easiest thing to do is remove PII as early as possible in the pipeline... At ingest or as soon as you possibly can in your system in order to minimize risk.”

Pro Tip: Sending customer data that includes PII to large language models (LLMs) like ChatGPT is a bad idea and will likely compromise user and confidentiality agreements with your customers.

Security

AI solutions may require access to sensitive data. Ensure that any solution provider maintains a comprehensive Information Security Management program to manage Sturdy’s systems and products’ security, availability, confidentiality, integrity, and privacy risks. The vendor’s program must be independently audited and certified to meet the requirements of Trust Services Criteria SOC2 Type II. You’ll also want to ensure that all data communications into and out of a platform are encrypted-in-transit. That data is stored encrypted-at-rest using industry-standard encryption mechanisms.

Any solution vendor should have a current SOC 2 Type II for your team to review. This is the most comprehensive SOC protocol and attests not only to the suitability of a vendor’s processes and systems but their operational effectiveness of sticking to those controls over a period of time.

Considering these technical requirements when evaluating AI solutions, you can ensure that the solution is compatible with your existing infrastructure and meets your performance needs.

AI is the future wave, and those who do not embrace this ever-evolving technology in their business are already falling behind. Jin argues,

We’ve already begun to see the paradigm shift, creating a new way of living and working with AI as a co-pilot... And yet, we’re still living in the wild west of AI, with land-grabbing occurring left and right.”

Those who do understand the immense power that AI can begin to automate processes, secure insights and increase customer engagement across multiple channels reap immeasurable rewards. As a CEO and business owner, Howard exclaims,

This is a big deal... this is huge.”

Unsurprisingly, AI has become one of the most aggressive sectors in business today. Still, fortunately, there are now plenty of resources and expert advice on how to safely and successfully deploy AI into your company. It’s time to get ahead of the competition and take advantage of this revolutionary force before it zooms us into an entirely new world of business opportunities!

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“100% of this article was written by a human” (True)

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We’ve all seen large language models (LLMs) rapidly move into the mainstream, and many of us are already using them at present to generate blog posts, sales emails, and marketing campaigns (sadly, not yours truly). 

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Indeed, there is no shortage of ideas and blog posts explaining how businesses will use LLMs to generate content and remove routine or laborious tasks. Some say LLMs will render coders and lawyers obsolete. (BTW, they won’t). While there will certainly be job disruptions, we believe the change will be one of radical productivity improvement and, as a result, much more interesting and fulfilling work for many of today’s knowledge workers.

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Before continuing, let’s clarify. LLMs are just one type of AI, a “tool.” If you think that auto-creating a sales email is a kind of “meh” endgame of billions of funding, you won’t hurt our feelings. That’s because we feel the future of AI in business will be utilizing many AI tools that will go far beyond creating content. They will help you think and take action. They will help you do things that are almost impossible or are very expensive to do today.

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If you love manually updating JIRA with bug reports, typing QBR results into spreadsheets every Friday, or getting five people in a room to discuss your best customer, then the adoption of AI for your business is going to be a bummer. For the rest of us, AI will make us much more productive, informed, and, ultimately, employable.

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Before continuing, I would like to say that I have spoken to many companies that have “started using AI in our business.” This always means dumping a healthy serving of emails and support tickets into GPT and generating summaries. And many now say, “That was cool, but now what?”

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They’ve just scratched the surface. At the risk of sounding like an AI hype machine, answering “What’s next?” is really difficult because we’re still trapped in an impossibility box: today, there are things we think are impossible but aren’t anymore. 

At Sturdy, business for AI will go beyond Generative AI, fundamentally changing how businesses collect, organize and synthesize their information. For now, I will call this AI Harmonization (AIH).  Again, this goes far beyond writing sales emails. Beyond the LLM, AIH will illuminate previously dark sources of data and harmonize that information across other models, thereby creating previously unimaginable strategic visibility and scale.

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The pieces of this AIH pie are the following: 

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1. Collect and flatten metadata, structured data, and unstructured data into a privacy-compliant, permissioned, and normalized structure;
2. Autonomously identify themes, topics, and insights inside this information and at its intersections. 
3. Automatically deliver “stuff” to, and synchronize with, other systems, workflows, and people who need it.
4. All of the above will be done automatically, in real-time, without supervision.
5. (extra credit): Your interface to this information will fundamentally change from “click here, then click here” to “tell me what you want.”

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Of course, what I just laid out vastly simplifies the challenge. Yet, I can’t help but feel super excited about what it means…here are some examples:

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If you are an Account Manager, your day might start with, 

“Here are three accounts you need to look at right now.”

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Or, let’s say you’re a VP of CS, 

“Let me know anytime an enterprise customer has an issue within 90 days of their renewal date. Check this every day at 9 am.”

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Or, imagine being a new VP of Products at a SaaS business, 

“Every Tuesday, send me an XLS with a product roadmap with all bugs, organized by topic, reported by our enterprise customers worth more than 500k in ARR ranked by unhappiness generated and estimated engineering complexity.” 

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Sure, many of the examples above are already completed in many well-run companies today. They are just being done with manual labor. A lot of manual labor. In fact, our Sturdy data shows us that as much as 45% of an Account Manager’s day is data entry. Logging tickets, updating salesforce, writing call summaries (that number doesn’t even count the “account review” meetings). 

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Yes, AI will eliminate almost all this manual labor masquerading as knowledge work. 

It will automate almost all data collection… reporting…and, eventually, much of the response. 

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But, worker productivity will skyrocket and allow our teammates to focus on much more meaningful tasks.

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(Shameless plug: you could buy Sturdy today and next week 3 of the examples shown above are now part of your business. (What, you think I write blog posts for my health?))

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In short, the people interacting on a day-to-day basis with customers will have about twice as much time to do high-value things, like talking to customers. If I had to guess, I would say that this probably means companies will have fewer Account Managers, but it also means that they’ll become twice as valuable (because they’ll be 2x more productive).

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If you were hired as an Engineer in 1970, it is likely that for the first decade of your professional career, you spent your days using a slide rule to double-check someone else’s work. Did a computer take the engineer's job? No. Did their jobs get better, more productive, and more important? You betcha. 

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Let’s do this. 

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Account views in Sturdy, the leading AI for Business platform, are specific views or displays of all the interaction data related to a particular customer account or group of accounts.

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Account views are designed to provide a comprehensive and consolidated view of all interactions and touchpoints the business has had with specific customer accounts or groups of accounts. This allows cross-functional teams to better understand the customer's needs, preferences, and behaviors, which can help teams develop more targeted and effective strategies to engage, expand, and retain the account. 

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Account-based views in Sturdy are helpful for several reasons:

1. Focused view of customer data: Account-based views provide a focused view of all the data related to a specific account or customer. This allows teams to understand the customer better and personalize their approaches.
2. Better alignment of cross-functional efforts: With account-based views, sales, CX, support, product, marketing, and operations teams can work together to identify the needs and pain points of each account and develop tailored strategies to address them. 
3. Improved collaboration among team members: Account-based views enable teams to share information and collaborate more effectively. Having all relevant customer data in one place allows team members to communicate easily and work together, giving the customer a better experience. 
4. A new collective reality: Account views create a shared understanding of the customer's history, preferences, needs, situation, and requirements. This can be especially important when dealing with complex or long-term customer relationships, where multiple individuals or teams may be involved in the sales and service processes. 

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Overall, account views in Sturdy will play an important role in creating a shared understanding of customer accounts and helping organizations work together more effectively to meet customer needs and achieve desired outcomes.

Every business wants to stay ahead of the competition. We’ve got a saying here at Sturdy; “your customers are either growing with you or away from you.”  And, if you think about it, you are just trying to develop a relationship with your customers to create trust and loyalty. To that end, one of the fundamental tenants of any healthy relationship is listening. Yup. It’s that simple. 

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One of the clearest paths to maintaining a competitive edge is simply listening to your customer’s feedback. The next step is acknowledging that their feedback matters. And the way to solidify the relationship with your customers is to implement their suggested changes in a timely manner.  

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At the end of the day, listening is a choice in a relationship. Whether or not you listen to your customers is up to you. But one thing is for sure. If you don’t listen to your customers, somebody else will.

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Unfortunately, listening to your customers is harder than it sounds — especially at scale. We wouldn’t write this blog post about it if it were easy. While we all agree that customer feedback can give you a competitive edge, implementing the suggested changes is not always easy. After all, customer feedback is often subjective and open to interpretation. It can be hard to take a risk on an idea that may or may not pay off – especially when your competition is doing something different. But the truth is, taking customer feedback seriously and incorporating it into your everyday processes will be hugely beneficial. Not only will you gain customer loyalty and loyalty from potential new customers, but you’ll also stand out in a crowded marketplace. Taking customer feedback on board might be difficult, but it’s worth it. 

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You might ask, “how do I listen to my customers better?” Relying on outdated survey methodologies like NPS and CSAT can be tempting. After all, these methods have been around for a long time and are tried-and-true customer feedback techniques. But the truth is nothing is more valuable than the unsolicited, unabridged voice of the customer. Relying on tools of the past, like surveys, can mean missing valuable customer insights, alienating good customers, and wasting valuable internal resources that could be focused on more high-impact projects. As we mentioned in our previous post, 4 stars and frustrated | time to move beyond surveys and sentiment, surveys continue to fall short for many reasons:

1. Surveys are a backward-looking tool in an era where customers expect near real-time remedies.
2. Survey results are often ambiguous, failing to reveal the cause of customer frustration.
3. Survey data is often seen as unreliable and not contextually substantive enough to drive real business impact.
4. Surveys are often answered by users with exceptionally positive or negative experiences. (According to Forrester reports, surveys capture between 2% and 7.5% of customer interactions.)
5. Survey responses are limited to structured questions, so respondents cannot provide feedback about topics not covered. 
6. Surveys require significant customer time and effort and can be considered annoying.
   
   
   

Don’t get us wrong, surveys can be a relatively simple and inexpensive way to collect customer feedback. But the truth is, they’re over the hill. The NPS was first published the same year the camera phone was created. Think that’s wild? The CSAT was created the same year the internet was invented. You heard that correctly, the world wide web kicked off the same year the CSAT was first administered. Feel old yet?

You might be thinking, “Okay, but what about the other methods of gathering customer feedback? What about focus groups, customer interviews, and journey mapping, for example?" Good question! These are decent ways to collect detailed customer feedback without relying on traditional questionnaires and surveys. There’s still one glaring issue, however… These methodologies are still looking through the “rearview mirror.” These reports, interviews, and maps capture what’s happened in the past. Your team needs to look forward through the “windshield” and see around the corners along the way.

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Today, deploying a commercial-ready artificial intelligence solution is the key to staying ahead of customer needs and competitors. It fills in the knowledge gap between customer feedback and your team by gathering and making sense of the  unbiased, unabridged, and unsolicited voice of the customer. By leveraging AI, you can gain insights that traditional customer feedback techniques simply can’t provide – like specific signals. For example, today’s AI solutions have language models that understand specific scenarios and integrate with large language models like ChatGPT to summarize what customers are saying autonomously. Surveys aren’t going to surface risks and opportunities in real-time. You and your team will have to sit down and read the results or pay someone to do it. AI is the only way to understand what best action needs to be taken in real-time. 

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Think about the potential application of a technology like this! This goes beyond customer success and truly impacts all aspects of a modern business. For example, AI solutions let your product team maintain product-market fit by autonomously capturing product feedback like feature requests, user confusion, frustration, etc. Customer intelligence can also discover and inspect product-related topics like performance issues, bug reports, access issues, security alerts, etc. Your RevOps and BI teams can access an entirely new structured data source to create analytical frameworks. Your marketing team can tap into your pool of happy customers for testimonials and case studies. The list goes on…

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In short, customer feedback should always be taken seriously. While outdated survey methodologies like NPS and CSAT can still provide insights, these techniques should only be used to supplement more modern strategies like AI-powered resources. By taking customer feedback seriously and relying on customer-centric methods, you’ll ensure your customers grow with you, not away from you.

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Churn is the biggest threat to growth for B2B businesses having recurring revenue models. Therefore, keeping a watchful eye on key revenue metrics like  account growth and retention is critical for executives.  Real-time dashboards are essential for executives as they provide visibility into their business performance. Dashboards help executives quickly gain insights into their key performance metrics and spot potential trends or issues before they become major problems.

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We identified a trend after speaking with dozens of executives at customer-obsessed companies over the past year or so. Leaders and board members want key revenue metrics available with one click. They neither have the time nor the need to go into the deepest levels of data. They want a quick way to access topline revenue stats and relevant data to inform conversations with revenue teams. 

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The all-new Sturdy Executive Revenue Dashboard, now in beta, makes powerful revenue analysis accessible anytime. It provides a quick way for the management to visualize and understand the following:

• account growth
• cancellations 
• month over month cancellation trends
• churn rate
  

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Sturdy’s new Executive Revenue Dashboards allow execs to automatically gather, organize and analyze the revenue metrics that are most important to the organization in one simple dashboard. Benefits include:

• A concise executive revenue summary – Executives get a consolidated report of key revenue metrics in one pane of glass. 
• Visualize trends –  A quick and effortless way for executive management to visualize the most critical trends, including growth, monthly cancellation trends, churn rate, and retention rate.  
• On-demand - With Sturdy, execs never have to wait for monthly or quarterly reports on the business's health. Access critical revenue-related trends anytime on demand with one mouse click. 

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Interested in learning more about how real-time revenue dashboards and churn dashboards can help executives? If so, book some time with one of our experts. During the demonstration, our expert will show you how our dashboard solutions can provide visibility into your business performance and enable you to take proactive steps toward reducing customer attrition and driving long-term growth.

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Product research is a crucial component of successful software product development. By understanding customer needs, preferences, and behaviors, technology companies can create products that create value for their customers and differentiate in the marketplace. Research helps businesses learn more about their target audience and users’ desired outcomes to develop features and functionality that increase customer engagement and dependency. Let’s face it, the name of the game is getting your customers addicted to your tool or platform. In addition, software product research provides valuable data that businesses can use to optimize customer acquisition and retention motions. 

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Traditional product research 

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To date, product research has been conducted through surveys, focus groups, and customer interviews. Traditionally, surveys have been emailed to customers immediately to gather qualitative and qualitative feedback. More recently, product experience platforms have given product researchers access to more dynamic in-app surveys, product usage analytics, and the ability to launch traditional surveys with fewer resources.

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Customer interviews allow one to ask specific questions and dig deeper into customer motivations, pain points, and specific use cases. Interviews can be extremely useful when businesses try to develop new products or determine how to enhance existing ones. Customer interviews can also provide valuable insights into desired integrations, services, and more. 

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Focus groups allow companies to observe how customers interact with products and better understand the user experience. Observing customers using the product can provide valuable insights that are unavailable through surveys or customer interviews. Additionally, observational research, such as shadowing customers in their own environment, can help uncover valuable insights that would otherwise remain hidden.

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Here are a few other common ways teams conduct product research:

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• Examine competitors: Analyzing competitors' products and marketing strategies can give you valuable insights into customer preferences and behavior trends in the market.

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• Track sales data: Tracking sales data such as purchase histories, customer feedback, and website analytics can help you pinpoint which products are selling well and which are not so you can adjust your product design accordingly.

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• Monitor social media: Utilizing social media channels like Facebook, Twitter, LinkedIn, and Instagram can help you monitor customer conversations about your product or service and see what users are saying about it.

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At the end of the day, what do all of the traditional product research methods have in common? They are labor-intensive, expensive, and time-consuming, requiring intricate expertise and specialization to operate. Another drawback to traditional product research methods is that the data and insights generated are typically used by a small group and not leveraged across the enterprise. 

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AI is Changing How Teams Conduct Product Research

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ChatGPT, the AI-powered natural language understanding (NLU) platform that helps automate conversations has catapulted AI into the business mainstream. Aside from being all the rage, business leaders are adopting AI now more than ever because of technological advancements that have made it more accurate and faster to deploy. Additionally, AI is becoming increasingly affordable, allowing businesses of all sizes to benefit from the latest advances in artificial intelligence. Furthermore, the increased availability of data has allowed for more sophisticated algorithms and models to be used, enabling better decision-making and providing a competitive edge for businesses that use AI. 

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Product leaders recognize that customer expectations are changing rapidly, and AI can help them stay ahead of the curve. While AI and its practical applications are evolving quickly, here are a few ways that advanced data sciences are already impacting product research.

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1. Automating the data capture and cleaning processes

AI automation can take over mundane tasks such as data collection and normalization (cleaning or standardizing data for reuse and analysis), freeing up teams’ time to focus on more strategic initiatives. AI also facilitates the data cleaning and preprocessing (data joining and integration) activities required to glean knowledge from the raw data. 

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2. Eliminating privacy concerns

Privacy issues are often a roadblock for product researchers. Teams must be careful how they use personal data (PII) to discover product insights. Privacy restrictions and personal data limitations challenge legacy experimentation and research methods. AI is paving the way to alleviate these concerns so teams can move quickly. New advances in  PII Identification, de-Identification, synthetic PII generation, and pseudonymization provide teams with tools to iterate and innovate faster than ever without jeopardizing privacy regulations. 

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3. Making sense of previously untapped data sets

AI-powered platforms are making it possible to sift through data using natural language processing (NLP) and machine learning algorithms to quickly analyze large amounts of customer-generated information like email, tickets, call transcripts, and more. These data sets have, for the most part, been hard to access given, among other things, their unstructured nature. AI-based tools can search for patterns and recognize key signals that might be difficult and even impossible for humans to spot, especially at scale. 

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AI is already accelerating product research by enabling teams to quickly and accurately collect, clean, and identify trends in customer behaviors related to product usage and specific future use cases. AI-based platforms can analyze vast amounts of data in real time, helping companies make decisions faster while reducing costs associated with human labor. Additionally, using natural language processing (NLP), companies can automate text-based research tasks, such as discovering specific product-related insights, which would otherwise take an immense amount of time and resources. With the help of AI, teams can gain valuable insights into their products more efficiently and more effectively than ever before.

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More Signals! More insights! More knowledge!  Today, we’re excited to announce the release of three new Signals designed to help our customers better understand their customers and what to know, now. As always, the new Signals were inspired by Sturdy’s existing customers and their feedback. 

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Introducing the “Apology”, “Discounting”, and “Cost Cutting” Signals. Designed and built by our data engineering team, the new language models detect the following:

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• When your internal teammates apologize to customers
• When discounts or price reductions are discussed with customers
• When customers ask to cut costs or reduce spend 

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Sturdy is the only customer intelligence platform with out-of-the-box, purpose-built language models. Adding these three new Signals brings the total number of Signals available to Sturdy customers to 23. Sturdy customers will be able to take advantage of these new Signals on Feb 15, 2023.  

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Apology

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This Signal detects when a teammate apologizes to a customer. This Signal takes directionality into account and only “signals” on outbound interactions. 

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For example, when a teammate says something like, “we sincerely apologize for just getting a response out to you now,” in a support ticket, a signal is being sent. The teammate apologizes for dropping the ball. Maybe this is an isolated issue. Or, if this is a common occurrence, it could be a problem and, ultimately, detrimental to the relationship. 

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Discount

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This signal detects when a discount or price reduction is discussed with a customer. This signal takes directionality into account and only “signals” on outbound interactions. 

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For example, when a teammate says something like, “I was approved to offer a 15% discount,” in an email, a Signal is sent. The teammate is providing a price reduction. Alone this may not be critical, but in the aggregate, discounting can be a bad habit for account management teams

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Otherwise, Sturdy shows details about specific accounts. It’s always informative to know if any teammate has offered a customer a discount — and when, and, most importantly, why. Sturdy surfaces this information in easy-to-read dashboards, so you don’t need to wade through your CRM, CSP, or ticketing system. 

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Cost Cutting

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This signal detects when a customer is looking to cut costs or reduce their spend. This is another directional signal that only fires on incoming interactions 

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For example, when a customer says something like, “We've loved the platform so much, but we are trying to reduce costs as much as possible” in an email, a signal is being sent, and often swift action needs to be taken to solidify a renewal, spot a trend, or answer questions like — what segments are asking for cost reductions, etc.

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Discovering and delivering customer Signals at the right time helps teams understand what needs attention — know, now. Survey and health scores don’t give teammates the knowledge of what to do now. Signals uncovered from everyday interactions with your customers are insanely relevant — a must have.  In today’s competitive SaaS environment, the most successful companies are learning to “listen” and interpret the Signals that their customers are giving them about their products and services. The category-leading companies are doing this at scale - automatically with Sturdy.  

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Catch your interest? Want to see how it works? Get in touch. 

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A guide to leveraging modern technology to build an actionable voice of the customer program.

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Every business benefits from knowing what customers think and feel. A Voice of the Customer (VoC) program can help you capture and leverage customer insights to improve your products, processes, relationships, and bottom line. VoC programs have been in existence since the dawn of marketing. However, until recently, they were limited to gathering data through surveys, interviews, or focus groups. Most VoC programs fail because they rely on yesterday’s tools to address today’s challenges.

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Surveys still fall short

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Most companies still rely on surveys to gather customer insights. Sure, surveying customers sounds like a good idea. To some extent, surveys are a good starting point for obtaining information about customer experiences. But let’s face it, we all know that survey response rates are low. According to Delighted, a good survey response rate ranges between 5% and 30%. This means that your analysis through surveys represents only a fraction of your customer base, and typically, only the dissatisfied or extremely satisfied customers take the time to respond. Unfortunately, most VoC programs still rely on surveys as the number one data source to influence decisions about products, marketing campaigns, service processes, and more. 

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Social media monitoring - meh

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While social media monitoring can be a great source of customer data and insights, it has flaws. There are several reasons why it may not always be the most reliable source for customer insights. First, as with surveys, social media users’ opinions change rapidly due to the nature of the platform. The same user may have different views or opinions at different times, which can lead to issues with reliability. Companies must ensure they are looking at a large enough sample of customers and not just basing their decisions on a few users' whims. Second, as we’ve learned from politics, all sources on social media are unreliable, and there is no way to verify their accuracy or truthfulness. VoC program managers can be misled if they rely heavily on these sources without doing extra research. And finally, as with surveys, monitoring conversations on social media is a time-consuming process. Companies must dedicate resources to this task to keep up with the latest trends and conversations about their brand or products, which can be costly in terms of both money and time.

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Focus groups flop

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For decades, businesses have relied on focus groups to learn more about their customers. Unfortunately, focus groups flop in many of the same ways that surveys and social media monitoring fail to deliver actionable insights. First, focus groups are typically limited in size and scope, making them unsuitable for gathering insights from a large customer base with diverse segments. Second, running focus groups is costly and resource intensive. This makes it difficult for companies with limited resources to benefit from them.

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The trends to watch for when building a modern VoC program

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Listen, if you rely on surveys, social media, and focus groups as the main inputs for your voice of the customer program, you are not alone. These methods are still the standard. But, there is a new trend emerging driven by advancements in technology.

Innovative businesses are starting to use traditional channels of customer feedback in combination with unsolicited feedback to gain true insights into VoC.  

VoC programs have come a long way since their inception, from manually collecting data through surveys and interviews to leveraging AI-driven analytics tools today. Technology has revolutionized how organizations collect, analyze, and deliver customer insights to the teams that need them most. With modern tools and platforms, businesses can collect, analyze and leverage data on a larger scale and with greater accuracy than ever before. Here are some ways technology has changed VoC programs:

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AI-driven signals 

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AI has revolutionized analytics tools over the past few years by allowing companies to collect large amounts of data quickly while also uncovering signals about specific customer behavior that were not possible before. Going beyond just sentiment,  AI-driven signals help organizations develop strategies that meet customer needs better and lead to long-term success. But the real power of AI is to deliver the signals that are happening now — ones that can impact this quarter's results! 

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Automation

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Before, businesses had to manually enter data into various formats and generate time-consuming and backward-looking reports. But with the combination of AI-driven insights and automation, teams can now automate processes such as collecting the unabridged, unbiased, and unsolicited voice of the customer. Automation, in this sense, reduces costs and frees up resources while increasing the speed at which teams receive valuable customer feedback. 

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Data integration 

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Modern customer intelligence platforms can combine multiple data sources to help VoC teams get perspective, providing a richer understanding of customer signals and trends from multiple channels. Using multiple data sources in combination with machine learning algorithms, companies can create more accurate models and insights than they would have been able to do with just one data source. For example, imagine having a searchable interface on top of every inbox, video call, ticket, and survey — a single pane of glass, as it were — a window into a real-time understanding of your customers’ needs and preferences. 

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It’s time to modernize your VoC program 

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The success of any VoC program depends on selecting the right tools and technologies for collecting, analyzing, and interpreting data. Companies need to consider factors such as cost-effectiveness, scalability, accuracy, and speed when building and updating VoC programs. Here are the considerations to get you started. 

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1. Collect more relevant data sources

Don’t stop surveying, scouring social media, or conducting customer interviews. Gathering multiple data sources is key. But it’s time to add data sources. Customer intelligence technology is maturing quickly. Many of today’s systems allow you to create omnichannel customer experience insights by capturing and analyzing every customer interaction, regardless of channel (phone, email, chat, etc.).   

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2. Analyze and interpret customer data 

Once relevant data has been collected, teams must analyze it effectively to draw meaningful conclusions. This requires the effective use of AI technologies such as natural language processing (NLP) or computer vision (CV). Effective analysis helps uncover signals and patterns that wouldn’t be visible from just looking at raw numbers or statistics like the results of surveys. 

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3. Deliver what matters - now

Finally, companies should use the signals gained from the analysis process to take actionable steps to improve their services or operations to better serve customers’ needs. This could involve implementing changes based on customer feedback or altering marketing strategies according to changing trends in customer preferences.

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Overall, creating a modern VoC program is essential for businesses in today's competitive market. By understanding its fundamentals and leveraging advanced technology, companies can gain valuable insights that can help them succeed.

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I was talking with a VP of CS not long ago, and she said, “Our AMs need Sturdy to tell us what to do next.”

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Since VC firms love to ask things like,  “Does your product recommend Next Best Action?” and Sturdy just recently closed some funding, my judgment was cloudy…

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I responded:

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“Do you mean that you need Sturdy to tell your people what to do next? Like if they hear that their account had an Exec Change, then Sturdy needs to give them a playbook?”

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“Uhh, no, our people know what to do next. We need Sturdy to find out what to do now….For example, if someone contacts the billing team and asks for a copy of their contract, we want the CS person to get an alert because right now, they might never even know their account is at risk.”

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“Now” before “Next”.

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I couldn’t help but think of all the different events that are spread out in other people’s inboxes. All of those “Nows” waiting to be found. (FWIW, we know that at least 15% of all customer conversations have some sort of “Now” in them)

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More Examples

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If one of your Account Managers gets an email that reads, “Hey, this feature is really confusing and annoying!” your UX Designer has a “Now!”

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If a customer responds to a ticket, “That’s really disappointing, we were sold this feature, and now we’re learning it does not exist. Lame!” your Sales Team has a “Now”.

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If a customer contacts your billing team and asks, “Hey, can we cancel our contract three months early?” then that customer’s Account Manager has a “NOW!”

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So, what does this mean for Sturdy? Well, we need to rethink two parts of our product. First, we need to make it much, much easier to sign up for the “Nows” that are important to you. Second, we need to ensure that all those duplicate messages in inboxes, chats, cases, and tickets don’t create duplicate warnings. No noise, just Signal.

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And we’re building this right now.

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We’re fired up to announce the launch of two new Signals designed to help customers gain more insights about their customers. Inspired by Sturdy’s existing customers and developed by our data engineering team, the new underlying language models detect when end users are confused and having trouble with billing-related matters. 

The addition of these two new Signals brings the total number of Signals available to Sturdy customers to 20. Sturdy customers will be able to take advantage of these new Signals on Feb 1, 2023.  

Confusion

This signal detects when a customer indicates that they are confused about what is happening or unsure about how to accomplish something.

For example, when a customer says something like, “we have no idea what is causing this,” in a support ticket, a signal is being sent. The customer is confused. They are asking for help. Maybe this is an isolated issue. Maybe this customer needs more training. Regardless, it’s an opportunity to engage. Furthermore, if your customers are often confused, it indicates opportunities to improve both your product and services. 

Billing Issue

This signal detects when there is an issue regarding billing or payment processing.

For example, when a customer gets or responds to a message like, “this is to inform you that our attempt to collect your payment has failed”, a signal is being sent. Maybe they didn’t receive their invoice, and it’s a matter of having the wrong billing information. In this case, a simple fix is in order. Otherwise, this could indicate a larger problem associated with the relationship of the account.  Or, if your company receives lots of billing issue Signals, it likely means that you have an internal process that needs to be revamped. 

Discovering, classifying, and escalating customer Signals at the right time helps teams understand what needs attention — now. Move over surveys, sentiment, and health scores. This is real actionable stuff— the stuff your team needs to work on now.  In today’s competitive SaaS environment, the most successful companies are learning to “listen” and interpret the Signals that their customers are giving them about their products and services. The category-leading companies are doing this at scale - automatically with Sturdy.  

Catch your interest? Want to see how it works? Get in touch. 

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Customer Intelligence (CI) has become a critical tool for organizations looking to gain a competitive edge in customer engagement and satisfaction. By collecting, analyzing, and leveraging customer data at scale, businesses can make informed decisions that will help them better understand their customers’ needs and preferences. With the rise of advanced technologies like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), customer insights have become more accessible than ever before. As a result, the number of Customer Intelligence Platforms available today proliferates, with more sophisticated tools emerging each year. This article will discuss the top 13 customer intelligence platforms in 2023 across various subcategories, such as sales intelligence, product intelligence, health score tools, productivity tools, and support intelligence.

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What is Customer Intelligence?

Customer Intelligence (CI) collects and analyzes key customer-generated data to glean crucial insights, risks, trends, and opportunities. CI is heavy on integrations and often uses advanced data sciences like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP).

CI is about data — some you may have already been using and new data now available thanks to technological advances. To grasp the magnitude of Customer Intelligence, imagine if you could unite and analyze all your customer interactions — emails, tickets, chats, call transcripts, and community data. Now imagine harmonizing this new knowledge stream with data in your CRM, CSPs, and usage tracking systems to create new analytical frameworks, reports, dashboards, and critical workflows. That is the essence of Customer Intelligence.  

It goes without saying that core to any commercially viable CI solution is a sophisticated data privacy element. While our customers want you to use their feedback, suggestions, and more to improve the value they derive from your products and services, they also expect solutions built for the privacy-first era. They want you to fix bugs, make your product less confusing, build critical features and service them better. CI means better listening — active listening.

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The proliferation of Customer Intelligence platforms doesn’t come as a surprise. Customer Experience has emerged as a top concern amongst business leaders, with more than 87% of senior business leaders indicating that customer experience is the leading growth engine for their businesses. The investment community has also taken a keen interest in Customer Intelligence-related startups pumping billions of dollars into the space in the past 48 months. The funding has been distributed across a variety of categories and line-of-business-focused segments. Let’s break CI down into a more digestible conversation. 

Customer Intelligence is quickly growing into a broad category. Our research taught us that a burgeoning ecosystem of CI categories and segment-specific platforms go deep to solve unique customer-related challenges. Nearly every Customer Intelligence solution leverages advanced data sciences to provide a missing layer to today’s B2B GTM stack. Based on conversations with over 100 B2B product and customer leaders, the most beneficial systems are those that create a System of Intelligence. But no matter the application, it is clear that leaders are looking for deeper insights with which to create more durable and profitable customer relationships.  

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Customer and Product Intelligence

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US-based Sturdy represents a strong example of an innovative, commercially-ready, Customer Intelligence solution. Sturdy collects unstructured data sources like customer emails, tickets, chats, meetings, community data, and more via public APIs. It then restructures the data while also anonymizing it to address privacy concerns. The “clean” data is combined with other data sources like CRM data and then is unified into one searchable system that every team can use. Sturdy consolidates hundreds, sometimes thousands, of data silos, then employs AI, NLP, and ML to surface essential signals and themes that help teams improve products, relationships, and revenue. The platform has a no-code automation engine and a suite of APIs (Sturdy’s Data Exhaust) to route essential data and insights to the people, teams, and systems that need them most.  

CI systems like Sturdy can transform massive amounts of unstructured data (think email) into knowledge delivered autonomously to any business unit, team, person, or system. Sturdy makes insights accessible to end users and back-office analytics teams alike. Leaders are investing in AI-forward systems of intelligence because they see it as paving the path to taking customer-centricity to the next level. 

Who buys Sturdy?  

Customer and product leaders.

Pricing

Sturdy doesn’t list pricing on their website, stating, "Sturdy’s business plan is based on the volume of data you process and the Signals you use. We tailor our plans to best fit your needs, so please contact us for a custom quote.” It’s also worth noting that Sturdy has enterprise and SMB “quick start” plans. 

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Sales-Focused Solutions

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The most mature category of CI products are those designed for sales and other pre-revenue teams. The leader in the space, Gong.io, has pioneered the Revenue Intelligence category, which is closely related to Customer Intelligence. Sales-focused CI solutions primarily analyze recorded sales calls for coaching opportunities and conversational insights about customer buying behaviors. 

Gong makes mention that their platform can support customer success and marketing teams by focusing on moving them “closer to revenue.” Gong also can help managers use conversational insights to identify coaching opportunities for remote workers, as it seems with this entire category. 

Who buys Gong.io?  

Sales and RevOps Leaders at SMB and enterprise companies with significant BDR and corporate-level sales teams. 

Pricing

Gong has a lot of great content on their site for sales and RevOps pros, but, like most others, they don’t provide pricing information. However, their site says pricing is based on an annual platform fee and the volume of recorded calls. Others to watch in this category are Invoca and Databook. Both are taking innovative approaches to provide sales teams with Customer Intelligence.

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Invoca, like Gong.io, is a sales-focused platform that analyzes transcripts from sales calls to surface opportunities. The Invoca solution is called center-ready, and they list large customers like Verizon, Robert Half, and 1-800-Junk on their website. AI-forward technology provides the power to analyze all sales conversations, and the user interface provides multiple views of the overall prospect's journey and, often, beyond.   

Who buys Invoca?  

Sales, Call Center, and RevOps Leaders at B2C companies with larger agent-based, sales call centers.

Pricing

Invoca offers plans for both brands & agencies and pay-per-call marketers. They offer Pro, Enterprise, and Elite tiers in the former and Performance Professional and Enterprise in the latter. Neither list pricing on the website. 

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Another player in the sales-focused category is Databook. Databook provides “strategic enablement for account-based selling,” allowing teams to focus on more “doing” and less “planning.” Databook’s website classifies strategic enablement as “the art of leveraging information, process, and technology to successfully craft the strategies needed to drive effective sales execution.” This is all to say that they provide data to better inform and optimize your account-based sales process. 

To accomplish this, Databook leverages its proprietary data sciences tech to analyze publicly available data. It crawls all your accounts to provide and finds and ranks prospective accounts. Databook positions itself as an Enterprise Customer Intelligence Platform — another system of intelligence — to help you close more deals. 

Who buys Databook?  

Sales and RevOps Leaders at B2B companies with account-based sales and marketing motions.

Pricing

Databook does not provide any pricing information on its website. You can request a free demo on their contact us page.

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Support / Contact Center Intelligence

In addition to sales-focused CI, the support-focused call center category is very well represented in funding and product maturity. Companies like Observe.AI, Balto, and Forethought have raised $358MM to analyze interactions like support tickets and agent-managed phone calls. These solutions seek to reveal coaching opportunities, quality of service issues, sentiment, and compliance matters. 

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Observe.ai is a noteworthy solution in the Support / Call Center Intelligence subcategory. The platform analyzes agent calls and tickets. Then, using its proprietary conversation intelligence engine, it looks for what they call Moments, out-of-the-box and customer-defined themes. Consolidated views of all agent conversations and Moments give leaders good visibility into coaching/training and quality of service issues. 

Who buys Observe.ai?  

Call Center, Support, and Service Operations Leaders at B2C and B2B companies with larger agent-based support call centers.

Pricing

Observe.ai does not provide any pricing information on its website. Instead, the company offers live demonstrations to walk prospective customers through the platform and its features based on various use cases.

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Leaders evaluating Observe.ai should also consider evaluating Balto. Balto’s conversational intelligence solutions offer benefits to agents, supervisors, and leadership with the goal of improving agent performance. Their AI enables companies to train and onboard their agents faster with prescriptive content suggestions and triggers that alert supervisors of critical moments and coaching opportunities. Balto promises to ensure that “your agents will say the right thing on every call,” real-time guidance is programmed to assist agents with the next best actions and workflows. Balto’s secret sauce is the real-time alerts that managers receive when agents need assistance allowing teams to be as proactive as possible.    

Who buys Balto.ai?  

Call Center, Support, and Service Operations Leaders with larger agent-based call centers at B2C and B2B companies.

Pricing

As with the norm, Balto does not provide specific pricing information but allows prospects to elect for personalized demos.

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Product Intelligence

Product Intelligence is another healthy category of the Customer Intelligence space. These solutions aim to serve product and user experience teams with customer-generated insights related to product adoption and roadmap suggestions. Pendo and Aha! have been at it the longest and focus on collecting usage data and surveys. While an up-and-comer, Enterpret is building the next generation of customer feedback intelligence by leveraging the voice of the customer.

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Pendo is a category leader in the Product Intelligence segment. It combines your product’s feedback, analytics, and in-app guides into one workspace. Pendo solicits and collects qualitative and quantitative data to understand customer engagement and product efficacy. With tools to impact and measure product engagement to deliver content to users at critical junctures like onboarding, Pendo is a feature-rich product intelligence solution. This maturity extends to Pendo’s commercial motions. In short, they have plans and associated feature bundles to fit small start-ups and enterprises alike.

Who buys Pendo?  

Product Management, Product Operations, Product Marketing, and Operations leaders at small and large B2B and B2C companies. 

Pricing 

Pendo is one of the few vendors that offers detailed pricing information on their website featuring four separate plans: Free, Starter, Growth, and Portfolio. While the freemium offering allows users to get a taste of the power of Pendo, it offers a scant limit of 500 monthly active users (meaning your product users), product analytics, and in-app guides. 

The Starter package increases monthly active users to 2,000 and adds their Net Promoter Score (NPS) tool. This package costs $7,000 a year. In addition to these offerings, Pendo’s Growth plan provides Sentiment analytics and can be used in a single web or mobile app. And finally, Pendo’s Portfolio package allows users to use the software across unlimited web and mobile apps. In addition to sentiment analytics, it provides cross-app reports and portfolio summaries. 

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Where Pendo focuses on customer feedback, Aha! provides a platform for product road mapping. More of an ideation and product creation platform for product managers than feedback analysis play, it’s a surprise to us that Aha! doesn’t integrate out-of-the-box with Pendo. Integrating Pendo data requires a Zapier integration.

The Aha! suite offers a collaborative seven-step framework for the product development process The first step establishes a clear vision and goals. The Ideate phase captures brainstorms and crowdsourced ideas. The Plan phase helps users prioritize, estimate value, and manage capacity. Showcase allows users to share roadmaps and go-to-market plans. The Build phase allows users to deliver new functionality through agile development. The Launch step brings these new features to market. Lastly, the Analyze phase allows you to see your product come to life by tracking customer usage. 

Who buys Aha!?  

Product Management and Engineering leaders at small and large B2B and B2C companies. 

Pricing

Like Pendo, Aha! also offers a freemium option for their Aha! Create, a digital notebook for product builders. Interestingly enough, Aha! offers a free 30-day trial for its premium products. This allows users to access all features, easily invite colleagues to collaborate, and does not require a credit card upfront. Following the free trial, the Aha! Develop offers an agile tool for healthy development teams at $9 per user per month. Aha! Ideas is a comprehensive idea management tool that starts at $39 per user per month. Last but not least, the Aha! Roadmaps offering starts at $59 per user per month. 

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Enterpret, similar to Pendo, is building a customer feedback platform. Unlike Pendo’s approach, which leverages data from surveys and other solicitations, Enterpret looks at external reviews and internal interactions like support tickets. The platform then allows users to create and search a taxonomy to find and track product insights. Enterpret is equipped with semantic search capabilities making it easy to query keywords and topics. Their core offering aims to help teams prioritize product roadmaps, discover product gaps, and detect quality issues. The company was founded by software engineers and backed by notable investors.    

Who buys Enterpret?  

Product Management and Engineering leaders at SaaS companies. 

Pricing 

There is no pricing information available on the Enterpret site. Like many others listed above, prospective customers can fill out a demo form for more information.

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Productivity Tools

Productivity-focused CI apps like Theysaid.io (FKA ‘Nuffsaid) and Retain.ai help customer success teams understand which customers need the most attention and which are black holes for your resources. For example, Theysaid.io uses a proprietary engine to prioritize tasks that matter most and log information to other systems without app-switching. This might be particularly useful to teams that use an “at scale” or “one to many” approach to manage customers. 

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TheySaid bills itself as a modern approach to customer success platforms. Customer interactions are consolidated in a single workspace. The analysis is done on the aggregate data to find trends. Customers are asked questions as they interact with products gathering inputs that make up quantitative trends. When a trend hits defined thresholds, workflows are kicked off. This can be particularly helpful for teams that employ a one-to-many approach. 

Users of TheySaid create role-specific questions vetted by third-party experts and sent at specific times during the customer journey. Risks are then scored and given a label. TheySaid state on their website that getting started takes just a few hours.

Who buys TheySaid?  

Customer Success Leaders are at SMBs that have not leveraged a traditional customer success platform.

Pricing 

Although no pricing information is offered on the website, the demo form states that prospective customers can try TheySaid for free.

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Like Theysaid, Retain.ai aims to create a single source of record for every customer. And, like TheySaid, getting started is quite easy. Just select what applications, workflows, pages, and attributes you want Retain.ai to track. Have your teams install a browser plugin, and the system starts tracking things like time-to-serve, engagement, team productivity, and more. Customers receive a holistic view of customer engagement across all systems view dashboards. Retain.ai has some sample case studies on its website, but it's unclear what market segment the product is geared towards.

Who buys Retain.ai?  

Customer Success Leaders at B2C companies (based on their sample case studies).

Pricing

The Retain.ai website does not provide any pricing information. Those interested in learning more can fill out their demo form.

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Health Score Tools

Arguably, customer health score solutions appear more as an output of Customer Intelligence than a category. These solutions target SMB buyers who haven’t adopted a more robust customer success platform. Companies like Akita and Involve.ai analyze product usage, NPS, the number of support tickets, and customer sentiment and then, with the help of data science, ascribe a health score to your accounts. Similar to Theysaid, Involve.ai takes it further by recommending playbooks once an account reaches a certain health threshold.

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Akita is the go-to customer success software for SaaS businesses. Akita provides a hub for telemetry-based customer data, activity, and metrics. Beyond storing all the information, it lets customers set up unlimited alerts when certain criteria are met. Like Involve.ai, automated playbooks can be triggered in response to customer behaviors or attributes. This frees up valuable time to focus on high-value tasks. Beyond this automation lies Akita’s task management capabilities, built to provide a single and simple interface for workflows. Thinks of this as a workspace for CSMs

Who buys Akita?  

Customer Success Leaders 

Pricing

Akita offers three transparent pricing options. Start, Connect, and Customize offerings can be purchased on a monthly or annual subscription. Prospective customers are incentivized to go annual by saving 20% after 12 months. The Start plan offers basic features and costs $160/month (if billed annually) for up to three users. Each additional user costs $47.20 per month. The Connect Plan offers “powerful integrations for a scalable customer success strategy.” This plan costs $480 per month (again, if billed annually). Similar to the Start plan, this plan includes three users, with each additional user costing $63.20 per month. Last but not least is the Customize plan. This option requires connecting with an Akita representative to learn more about their advanced integrations. Before committing to any of these plans, however, prospective customers can test Akita out on a free 14-day trial. This free trial includes unlimited user licenses, playbooks, custom segments, and health scores.

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Involve.ai touts that they’re an early warning system to predict churn and upsell opportunities. Their platform is built to help customers capture and analyze customer sentiment. After organizing and analyzing customer sentiment, Involve delivers actionable insights regarding retention, churn risk and upsell opportunities. Additionally, Involve provides customers with an actionable customer health score powered by their proprietary AI model built to analyze customers’ qualitative and quantitative data. Like Akita, Involve provides automated workflows and playbooks to maximize team efficiency.

Who buys Involve.ai?  

Customer Success Leaders at SMBs that have yet to adopt a customer success platform 

Pricing

Involve.ai doesn’t provide a specific pricing breakdown but a tool that hints at potential costs based on the number of clients and revenue. For example, a company with a $5MM ARR, 2% Annual Churn Rate ($100,000), and fifty customers can expect to pay $12,000 annually for Involve.ai.

By now, it’s clear that Customer Intelligence is a diverse and quickly evolving market. This list is not exhaustive. The common theme for all the systems mentioned here is data centricity. They all hinge on getting data in one place and analyzing it to provide better insights about customer behaviors.   

Whether you’re already sold on the value of Customer Intelligence or looking for ways to take your customer relationships to the next level, check out these key considerations you need to know about choosing the right Customer Intelligence platform to accelerate your goals. 

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When choosing a CI platform, consider the following:

• Insights for various teams: Customer Intelligence isn’t just for customer success teams. Product and engineering teams can immediately benefit from learning more about customer frustration, confusion, and wants directly from the voice of the customer. Marketing teams can transform positive insights into customer references. Revenue operations and business intelligence teams can create new analytical frameworks from previously unavailable data. Choose a system that helps you democratize customer insights and one that helps to create a collective reality for every team that wants to better understand your customers.
• ‍Fast time to value: Let’s face it, we’ve all bought platforms that were oversold, hard to implement, and even harder to administer. Look for solutions that can deliver insights to your specific use cases quickly. Understand the resources required to start receiving value and what resources are needed to maintain the program in the future.
• ‍Tech stack: When choosing a Customer Intelligence platform, the platform you select must integrate deeply with the critical components of your current GTM tech stack. And don’t forget about customer email. More than 50% of B2B customer-to-business communications start with an email.
• ‍Avoid duplicate functionality: CI platforms often have similar functionality to systems you already have, like customer success platforms and CRM systems. Look to compliment your existing system with rich data from a Customer Intelligence solution.
• ‍Security: Does the platform have a clear and transparent take on data security? Ensure that any system you choose is SOC 2 Type II ready.
• ‍Data privacy: How does the platform handle data privacy? What is the technical approach to safeguarding your customers’ PII? Will the solution meet the security and privacy requirements of your infosec and data privacy teams?

In conclusion:

We’re still in the early innings of CI. The challenges to achieving the potential are eroding as quickly as the technical capabilities are evolving, creating a new must-have system for the modern post-sale tech stack. Many organizations aren’t aware of how rapidly it’s evolving and may not realize the benefits Customer Intelligence can bring to various teams in their companies.

As we look ahead to 2023, it's clear that Customer Intelligence will continue to be one of the most essential tools businesses can use to stay competitive and understand their customers better. By leveraging customer data through CI platforms, companies are able to make informed decisions that will help them improve customer engagement and drive sales and revenue retention. They ultimately increase customer satisfaction levels across all channels to ensure your customers grow with you, not away from you.    

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Customer retention is the ultimate force multiplier in any B2B SaaS business. It involves building strong relationships with existing customers, ensuring they stay loyal to your brand, helping them use more of your product or service, and becoming advocates who bring in more customers through word of mouth. By investing in customer retention and ultimately increasing your customers' lifetime value (LTV), SaaS businesses unlock tremendous potential for growth and profitability.

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Sometimes the SaaS world seems like alphabet soup. Lots of acronyms. As a reminder, Lifetime Value (LTV) is an essential metric for SaaS businesses. It measures the profitability of a customer over their entire lifetime of their contract or subscription. LTV provides an indication of how much revenue can be expected from a customer within any given point in time. 

Here’s how I suggest calculating LTV. First, determine the average revenue per user (ARPU). This is calculated by dividing total revenues by the number of users over a specific timeframe. Then, divide this result by the customer churn rate for that same period — this will estimate how long each customer’s subscription lasts on average. Multiply the ARPU and estimated lifecycle together to get your lifetime value. Doing so will allow you to accurately measure customer loyalty and help you devise meaningful customer retention strategies. 

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Over the course of my career, I’ve learned that sometimes the best strategy is to stop doing something rather than create a new process. Making changes and implementing new processes and workflows can be time-consuming, lead to more complications, and cause confusion for your teams and customers. Simply put, here are a few things you can do to stop pissing off your customers because we can all agree that pissing off customers is a bad strategy.  

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Stop ignoring customer feedback

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Ignoring customer feedback is more than a mistake; it’s negligence. Customer feedback is the single most valuable thing a customer can provide — arguably more than their contract value. Insights about your products or services allow you to make improvements and create better experiences for every customer and every prospective customer. 

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I’ve written about the perils of relying on surveys to capture customer feedback. So as a modern business leader, it’s high time you establish the channels to capture it and share it with the teams that can benefit the most. Have a system for everyone in your organization to access and analyze customer feedback — make feedback a collective reality. Democratize it. 

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At one company where I served as the chief revenue officer, we provided hiring software to medium-sized employers, which helped them attract job applicants and manage the interview and hiring processes. We monitored customer feedback carefully. In fact, we monitored feedback so closely that it became a part of our culture and was more or less the genesis of my current company, Sturdy. 

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In addition to fielding and responding to occasional issues and concerns about how our service worked, we identified patterns within the feedback: features that were missing, UI that was confusing, bugs that caused frustrations, coaching opportunities for associates, and more. These patterns in the customer feedback informed the creation of very focused rules of engagement and playbooks that ultimately increased our LTV. This lift in LTV helped us successfully sell that business to one of the largest payroll providers in the world. 

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Stop overpromising

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Whether the account manager said “yes” when they should have said “no,” or what they said was accurate until someone else messed it up, overpromising often comes back to haunt post-sales teams. Poorly aligned expectations leave everyone involved feeling disappointed and let down. This fracture in the customer-to-business relationship is one of the leading causes of cancellations. It’s also one that often goes undocumented or improperly categorized. 

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Just as important as capturing the reasons why customers cancel, customer success teams should identify and document common trends and topics that indicate overpromises. By understanding the areas where false promises are made, you can enable customer-facing teams to consistently provide accurate information about the capabilities of your product and services. 

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Shameless plug for Sturdy — Our AI looks for Signals of overpromises in communications with your customers. This Signal detects when a customer indicates a discrepancy between the product or service they expected and the one they received.

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Here are some overpromise signals that were detected in customer-business emails. Sound familiar? 

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"This is something that was promised in the implementation stage."

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"… even excited about the features that were promised. But do feel ... underdelivered on the capabilities."

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"Below is a list of things that were promised and hasn’t happened:"

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"That was promised, but I still have not received anything."

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"We can't use these services that were promised/promoted."

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Stop doing Silly QBRs 

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Ok. This may seem trivial and maybe even a little silly itself, but I can’t let this one go. For those unfamiliar with the term, a Quarterly Business Review (QBR) is a look into the performance and value of your service over the past quarter. The objective of a QBR is to identify areas of improvement and offer strategies for moving the relationship with your customer forward. As the name suggests, QBRs are typically conducted at least once per quarter and most often with a typical, boring format — a presentation on some slides.  The TLDR — 95% of the time, QBRs are awful. Personally, I loathe being on either end of them.

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I suggest taking a page out of Customer Success Keynote Speaker & Educator Aaron Thompson’s playbook and turning QBRs into something meaningful for your customers. Use them as an opportunity to strengthen your relationship. Don’t just go through the motions. Here are some other tips from Aaron’s blog post on LinkedIn titled “Stupid Is As Stupid Does...And QBRs Are In Fact Stupid

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1. Make them a conversation, not a presentation.
2. Come with more questions than statements.
3. Don't get into SLAs, IRTs, or anything tactical. The topic du jour is their business strategy, and you are there to learn, not to teach. 
4. Make them 50% retrospective and 50% prospective. 100% strategic still. 
5. Get Creative. Much like Spotify's #Wrapped2019 (and 2020 and 2021) campaign, they demonstrate value to their millions of subscribers at the end of each year at scale.

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At several of the companies that I’ve started, advised, consulted for, and worked at, we’ve used the ‘stop, start, continue’ framework. If you aren’t familiar, the ‘stop, start, continue’ framework facilitates retrospectives. The outcome is improving future work performance through open communication and collaboration. In that vein, if you stop doing these things that damage customer relationships, you will open up the possibility of developing deeper relationships with your customers based on trust and value. Implementing even one of these changes can significantly impact your customer retention strategy. Which of these are you going to commit to first? 

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If you’ve ever heard the following - keep reading

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"This is something that was promised in the implementation stage."

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"We were excited about the features that were promised, but you’ve under-delivered on the capabilities."

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"Below is a list of things that were promised and hasn’t happened:"

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"That was promised, but I still have not seen or heard anything."

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"We can't use these services that were promised/promoted."

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TLDR

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Sturdy discovers signals in everyday customer interaction like email and more. The Overpromised Signal detects when a customer indicates a discrepancy between the product or service they expected and the one they received.

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Whether the salesperson or account manager said “yes” when they should have said “no,” or what they said was accurate until someone else messed it up, overpromising often haunts post-sales teams. Poorly aligned expectations leave everyone involved feeling disappointed and let down. This fracture in the customer-to-business relationship is one of the leading causes of cancellations. It’s also one that often goes undocumented or improperly categorized. 

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Just as important as capturing the reasons why customers cancel, post-sale teams should identify and document common trends and topics that indicate overpromises. By understanding the areas where false promises are made, you can enable customer-facing teams to consistently provide accurate information about the capabilities of your product and services. In short, take these trends back to sales leadership to address the problem systematically. 

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Intrigued? It works. See Sturdy in action.  

Before Sturdy, we worked for a B2B SaaS Software company called Newton. At Newton, we spent an enormous amount of time tracking and recording customer insights that came from customer feedback. 

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In fact, we had a training program, Alchemy, where every person at Newton was trained on what to do when they read or heard certain things like, “how do we download our data?” or “can we get a copy of our contract?”. We had a rule that every “happy” customer was sent to marketing for a potential reference. Every unhappy customer got a call from an executive. We thought we were a well-oiled machine. And yet, with all this, whenever we wanted to get on a call with an important customer, we needed to get several people in a room to discuss the account because we could never be sure what state the account was in.

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The challenge was that logging and identifying these important account triggers was entirely manual. If we logged every email, it just became noise. If we logged nothing, we had no idea what was going on.

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And at Newton, we realized that in a year, we generated 15,000 support tickets, 15,000 phone calls, and almost 100,000 customer conversations via email. 

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Email. Almost every executive knows they have data gathering digital dust in email inboxes. Unread messages, Bug Reports, Cancellation Requests, and Unhappy sentiment are just a few examples of critical business signals that flash in and out of inboxes daily. The challenge is, and always has been, to ensure that every signal is recognized and acted on.

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When we started Sturdy, the idea was simple, “the way we record and monitor customer feedback is insane. It has to change”. So we decided to tackle customer email first. Along the way, we realized we had built the first “Customer Email Intelligence Platform.” 

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In building Sturdy, we learned that a customer email intelligence platform must do four things very well, all at once: 

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1. Safely and securely extract only customer emails while ignoring all other emails;
2. Accurately merge all of a customer’s information into one view, a “single pane of glass”; 
3. Classify, categorize and Identify critical themes, topics, and sentiments in each email;
4. Route and alert the teams and teammates who need to know.

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Safely and securely extract only customer emails while ignoring all other emails

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For a long time, technologists have developed technologies that attempt to extract customer email data from an inbox and put it somewhere more useful: Outlook plugins, BCC addresses, Salesforce logging, Activity Capture, and Do-Not-Reply Email Addresses. These systems often create more issues, like duplicated data, missing emails, and lost headers. 

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Modern CEI solutions will not rely on “hacks” like BCC to get customer emails. At Sturdy, we have a patent-pending suite of tools that ensure only emails from/to customers can be ingested. This toolkit also allows Administrators to ask Sturdy to ignore emails sent by certain people, or it can be restricted at the API-level. 

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Bottom Line: Extracting customer emails needs to be rock-solid, secure, and highly configurable.

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Accurately merge all of a customer’s information into one view, a “single pane of glass”

“Hey, I need to call Acme Corp. Let’s all get together for 20 minutes to review their account.” Having all your customer emails in one organized spot will make wonderful things happen. The most obvious and time-saving will be the virtual elimination of the “hey, what’s going on with this account meeting?” Getting together to discuss accounts will never go away. But, having a 20-minute meeting so everyone can share their email inboxes should.

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In fact, Sturdy estimates that in a typical B2B SaaS company, an Account Manager spends almost 30 hours per month in Account Review meetings. 

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Bottom Line: Moving customer email out of the inbox will vastly improve account management and add time to everyone’s day.

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Classify, categorize and Identify critical themes, topics, and sentiments in each email

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The third pillar of CEI is where the heavy lifting happens. Today, your business can convert and categorize every piece of customer feedback into something actionable or insightful, at scale, without manual labor.

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If you're considering using AI or machine learning, remember that almost all language models today are trained using consumer data. This means they weren’t trained using business language, which tends to be far more restrained and professional. 

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We have reviewed over 10 million customer emails at Sturdy and built language models identifying the key themes and topics driving B2B SaaS and Services businesses. We have found that over 20% of customer emails have an essential theme or topic relevant to another business team. 

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Bottom Line: Modern AI technologies will illuminate insights, topics, and themes in your customer base at scale.

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Route and alert the teams and teammates who need to know

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You have likely worked in a company that attempted an early version of email intelligence. It was just done manually.  “If you get a feature request in an email, log it to Jira and forward it to the engineering team.” Identify, Classify, and Route. Manual labor doesn’t scale.

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Imagine if every time a customer was confused by a product issue, it could be routed to the design team. Imagine if every bug report ever reported by a customer was searchable at its source. 

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As modern Customer Email Intelligence identifies and routes business themes and topics without requiring human interaction, the hidden costs of recording, saving, and logging customer requests will go to almost zero.

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Sturdy’s automation engine allows our customers to harmonize email intelligence with CRM data. So you can say, “If one of our top accounts requests a copy of their contract, let the CEO know.”

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Bottom Line: Customer Email Intelligence will ensure that the correct information gets to the right team every time.

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Customer email intelligence. The time is now.

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There’s never been a better time to upgrade your tech stack to include Intelligence solutions. Businesses can maximize productivity and accuracy by scaling these intelligence solutions while eliminating mundane and time-consuming tasks. This type of automation allows companies to scale quickly, adapt to changing markets faster, reduce costs and increase efficiency. New technologies like Customer Email Intelligence also allow for more intelligent decisions that can save time and money in the long run. Sturdy might be your solution if you want to understand your customers better at scale and remove manual labor from your business. Let us know.

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Whether it’s a positive review or a scathing complaint, customer feedback is critical to the success of every business. It’s a window into the experiences buyers seek and a way for B2B software companies to improve their products, processes, and relationships.

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Customer feedback is information given by your customers about the quality of your products and services. Are you meeting customer requirements and delivering value? Whether good or bad, there is no better and more reliable data source about your company than customer feedback.

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With B2B buyers demanding more B2C-style experiences, it’s never been more critical to keep up with the changing needs of buyers and users. Unfortunately, many teams still rely on yesterday’s tools to solve today’s challenges. 

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To date, most companies have relied heavily on surveys to gather feedback. Others have coupled surveys with analytics tools that analyze customer sentiment. Unfortunately, both surveys and sentiment analysis fail to provide the necessary depth of qualitative data to build deeper customer relationships. Simply put, surveys and sentiment are often subject to broad interpretation. 

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Today’s most competitive B2B SaaS companies are putting deeper contextual insights about their customers to work. They are doing this by layering them into operations, processes, metrics, information flows, etc., to enable every team to make decisions based on specific, actionable signals. We’ll explore this more later.

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Surveys are still the status quo

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Let’s face it, surveys are a relatively simple and inexpensive way to collect customer feedback. However, Forrester reports that surveys capture between 2% and 7.5% of customer interactions.

Given the importance of understanding our customers, SaaS businesses must expand their approach to collecting and curating customer feedback. This starts with expanding the data sources teams use to operationalize insights across the business.   

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Easier said than done. To date, B2B SaaS businesses haven’t invested heavily enough in tools and technologies to help them better understand their customers. Today, leaders still struggle to create a complete picture of customer needs, frustrations, and intent. To a large extent, this is due to a reliance on surveys.

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While many of us can’t rid ourselves entirely of surveys, they continue to fall short for these reasons.

1. Surveys are a backward-looking tool in an era where customers expect near real-time remedies.
2. Survey results are often ambiguous, failing to reveal the cause of customer frustration.
3. Survey data is often seen as unreliable and not contextually substantive enough to drive real business impact.
4. Surveys are often answered by users with exceptionally positive or negative experiences.
5. Survey responses are limited to structured questions, so respondents cannot provide feedback about topics that are not covered. 
6. Surveys require significant customer time and effort and can be considered annoying.

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Customer surveys are just one tool in the burgeoning field of customer intelligence. Sturdy defines it as the process of collecting and analyzing customer data from internal and external sources to unlock customer insights. Recently, many have turned to sentiment analysis to gain a deeper understanding of the consumer mindset. Sentiment analysis insights gathered from different sources lead to improved product features, pricing, customer experience, and overall customer satisfaction. 

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Sentiment alone is… OK

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Many companies are running sentiment analyses on their product or customer service feedback. But as with surveys, this isn’t enough. Sentiment analysis gives you the binary answer good/bad or extends the range with outputs like terrible/bad/OK/good/great. 

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Sentiment analysis requires machines to be trained to analyze and understand emotions as people do. Human language cannot be categorized into only three buckets (positive, negative, and neutral) in its intricacies and complexities. For example, Let’s say we determine that 68% of customers have a negative impression of our product. That still leaves us with many unanswered questions: Do we change the pricing? Do we make UX adjustments? Without more specific insights, we’re left, once again, to go with our guts. Think survey results. 

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Let’s put it differently: if 68% of your customers are expressing negative sentiment, you need to understand why the customer feedback is so negative. Your team will need contextual clues to solve this level of dissatisfaction. The answers are probably right there; you just need the qualitative layer below the actual sentiment. 

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Once you understand the qualitative data, you can design better products, adjust processes, and build better relationships based on specific data points that need less interpretation. To do this, companies are leveraging next-generation AI, NLP, and ML technologies that provide deeper, actionable insights about their customers. 

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Tapping a new source of customer feedback

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Customer insights programs are more successful when customer data and feedback are gathered from multiple sources to get a more complete, diverse look into customer needs and impressions. Companies realize that customers constantly send signals that help us predict churn, capture references, get in front of renewals, prioritize features, and run our businesses better. Our customers are giving us this information in Slack, Email, Salesforce, Webinars, training sessions, quarterly business reviews, Zoom calls, etc., daily.

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Customer Signal

(noun) A gesture, action, or transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights. 

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For B2B SaaS businesses, these signals are immensely valuable. For example, reducing churn from 10% to 9% in a $10 million ARR business means that every customer is worth $17k more in lifetime value (500 customers, $20k annual contract value). And reducing churn in this example is saving just 5 customers a year. 

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Examples of Customer Signals‍

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Identifying, classifying, and escalating customer signals to the right people at the right time empowers companies with information and insights to preempt issues before they spiral and seize revenue opportunities in time to improve the bottom line. 

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For example, when a customer asks, “Can I have a copy of our contract?” in a support ticket, a signal is being sent. In a SaaS environment, the customer is likely signaling risk. Maybe they are evaluating a competitor. Perhaps there has been an executive change or a shift in priorities. Regardless, every SaaS leader will agree that this signal needs to be escalated so action can be taken. 

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Below are a few other examples of customer signals. This is not an exhaustive list; every company will vary on what is essential. An interesting exercise is to sit down and list out the signals that your teams should be watching for. The output of this exercise can be used to improve operations, user experience, training workflows, and more.

Customer signals help us understand our customers better than surveys and sentiment alone. By defining and leveraging signals at scale, we can clearly understand if our products are delivering the value promised at the time of the sale. We can also better understand if our customers are willing to grow with us or are growing away from us. 

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“B2B companies historically lag behind their B2C counterparts in adopting and deploying commercial analytics, but the ones who engage with the tools already outperform their peers; their return on sales are up to five percentage points higher than that of their counterparts.” McKinsey

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New analytics tools like Customer Intelligence platforms reveal opportunities for cross-functional collaborations. And the insights often have significant implications for non-sales teams. Rapid advancements in technology, especially AI, are making it easier to help brands quickly and responsibly use data to understand customer behaviors and predict customer needs. We can better anticipate future decisions when we discover new patterns and insights in our data. Ultimately, going beyond surveys and sentiment by leveraging customer signals presents opportunities and incentives to deliver better service and find new ways to grow.

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We’re in the decade of data. Data products like Snowflake, AWS, Azure, and Google Cloud have created more market cap than any other segment of SaaS in the last five years. Unfortunately, the ripple effects have been slow to reach every business unit, especially customer experience teams — couple this with macroeconomic malaise, layoffs, and customers’ changing needs. The demand for deeper customer insights will be a top priority for B2B SaaS companies in 2023.

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Dirty data continues to be a drag.

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Let’s face it; when you don’t believe in the data, you quickly lose faith and are often forced to rely on your intuition to make decisions. The old expression “garbage in, garbage out” was coined nearly 50 years ago. However, we still struggle with data quality or what we refer to as dirty data. Dirty data is inaccurate, incomplete, or inconsistent data sets. 

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So what stands in the way of gaining more timely and accurate customer insights? You guessed it — dirty data. Brian Hall, President & Founder of Carema Consulting, states, “Dirty customer data is the biggest threat to successfully realizing a land & expand strategy.” Here’s how dirty data plays out for many customer experience teams. The current customer health score shows the customer is green. Usage levels are normal. The account manager had no risks flagged, but there’s no connection to the ticketing system to let the AM know there has been a spike in tickets over the past two weeks. Disconnected data. Unmatched data. Dirty data makes it nearly impossible for many teams to identify the early warning signs associated with risks. 

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Your customers aren’t going to tell you it’s about the data when they complain. Dirty data certainly isn’t going to show up in survey responses. But bogus data is the root cause of your customer problems. 

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In 2022, the team at Sturdy spoke with over 100 CX leaders and attended several CX-focused conferences. What we heard was consistent — teams don’t have the correct data to look through the windshield. Instead, we are still looking through the rearview mirror. Here are some of the most common challenges. Sound familiar? 

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1. The focus is on retention, but there’s a lack of available data to identify risks consistently.

2. Everyone wants higher unit margins, but most fail to employ automation effectively. 

3. More teams want to leverage customer data, but reliable sources are scarce.

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You probably saw plenty of 2022 predictions about sexier topics. “This is the year of digital transformation!” “The year of value creation!” 

While those things are essential, it’s just lip service without the right data. I predict that the data decade will continue roaring in 2023, fueled by the further adoption of data management solutions and the growth of Customer Intelligence Platforms. Such platforms will turn data into the insights teams need to create more long-lasting customer relationships. 

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Here are the top trends I am watching for in 2023. 

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Dirty customer data gets its day.

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Dirty customer data is the root cause of most customer-related issues. And to compound the pain, tech solutions that would typically solve data issues, like artificial intelligence and machine learning tools, require access to accurate, high-quality data. The old chicken and the egg problem. 

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This year we’ll see more B2B SaaS companies taking a more strategic and systematic approach to customer data management. While it will primarily be a people and process challenge, more customer intelligence companies will enter this space, especially as the need for tools to provide solid data quality and analytics solutions continues to grow.

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Customer data democratization grows. 

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More businesses will adopt customer intelligence solutions to provide self-service-oriented insights across multiple business units. The trend here will take the pressure off traditionally under-resourced CX leaders to be the clearinghouses for all post-sale customer-related data. 

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This year BI and CX ops teams will focus on building new analytical frameworks with corresponding data. Product teams will start to have access to the unbiased, unabridged voice of the customer. Marketing teams will more intelligently identify customers willing to be priceless advocates.

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Data-driven insights overshadow surveys.

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More and more, a leading B2B CX strategy is to use data to discover what customers are doing and saying every day instead of focusing on surveys. Like many metrics we use today, interviews and surveys rely on customers’ recollections, which are always backward-looking and biased. Data-driven insights provide an impartial lens into customers’ actual words in near real-time. 

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I predict more leaders will dispense with the survey charades. We’ll see more teams begin to rely on insights derived from customer ecosystem data to identify risks and opportunities to improve the customer experience. 

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The activation of AI-fueled automation.

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Automation is the future for many business units. As more mundane tasks are automated by machine learning and AI, humans have increasingly more time to devote to developing relationships with customers. AI can also simplify data unification by providing more streamlined, intelligent processing. 

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With its ability to comb through big data sets like customer emails and tickets at faster speeds, AI-forward Customer Intelligence Solutions will enable leaders to service the one-to-many segments successfully. Brian Hall exclaims, “email and tickets represent a largely untapped treasure trove of customer insights for B2B SaaS companies. Those companies that leverage these insights have the best chance to consistently grow customer lifetime value.”

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Get ready to leave dirty data in the dust. 

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Over the last few years, we have seen some paradigm-shifting evolutions in data technology. The near ubiquity of unified data management systems (data cloud products as described above) has made it possible for many of us to collect, combine and consume new data sets. 

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Businesses will put a dent in dirty data this year, driven by AI-fueled innovation. Here is how:

• More business units will benefit from customer intelligence solutions that provide democratized customer insights. 

• The unabridged, unsolicited voice of the customer will ring with accuracy, replacing the stagnant straw polls and surveys that teams have long relied on. 

• AI will surface contextual data in real time, making automation a reality for scaled customer experience teams. 

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Data-driven power shifts are redefining customer experience. It doesn’t matter if your business has bought into them yet, the shifts are happening either way. 2023 is the year that many CX leaders will start to leave dirty data in the dust.

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Since starting Sturdy, we have learned that about 50% of support tickets and 15% of emails contain a roadmap-informing data point.

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I have worked on the product side of software for about 20 years, and the most common question from management is, “What are we building next?”. It is a question that I ask myself almost every day.

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Answering “what to build next?” isn’t always easy, but explaining “why we are building this next?” never is. 

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(Inevitably, engineering will want one thing, support another, and sales yet a third. But I digress.)

We were not venture-funded at Newton Software, so building the wrong feature could have killed us. We took these decisions very seriously.

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We had 3 weekly meetings to inform our “why build it?” decisions. Our Support Team leaders were in charge of mining tickets for the most common bugs, “how do I do this?” items and feature requests. Our Customer Success leadership was responsible for capturing similar data, mainly found in email. And finally, there was a third meeting with Sales leadership, where they informed us of the features they need to close more deals. 

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In other words, we manually harvested data from multiple data silos and teams to inform product development decisions. This data was in support tickets, emails, chats, and phone calls. Someone would need to manually record data in something like JIRA or Salesforce to even have it. If they didn’t record it, we didn’t get it.

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Effectively capturing data to inform product roadmaps is probably the most important thing a software company can do. As product planners, we rely almost entirely on other teams to manually source, process, and organize this data. The teams have other jobs though…

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After Newton was purchased and we were in a larger organization, it became apparent that manually converting conversations into data was too time-consuming and expensive. It didn’t happen. As a result, we had almost no data informing our product decisions. 

That’s why (after exiting Newton) our “What do we build next?” question was answered with, “Let’s build something that turns all of our customer feedback, tickets, conversations, and emails into some real data!”

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Every time a customer contacts your company, they want you to listen. You want to listen. Take this simple challenge: count the number of new support tickets you got last month and cross reference JIRA. Did 50% of those tickets turn into data? Now try the same thing with email. We know it’s not easy.

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By turning all of this feedback and information into data, product planners can access and employ the voice of the customer to make informed product decisions. If making more informed product decisions is essential to you, give Sturdy a look. 

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Don’t hesitate to contact me at steve@sturdy.ai if you have any questions or comments.

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Valuize's Chief Client Officer, Emily Ryan, invited our very own Joel Passen to discuss data hiding in plain sight. Hosted on LinkedIn live, the Valuize team has been kind enough to share this excellent content with us.

Hosted by: Emily Ryan, Chief Customer Officer at Valuize

Initially hosted on LinkedIn Live

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Interview Annotations

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1:15 | Introduction

2:20 | Icebreaker

4:10 | When I say customer success operations, what's the first thing that comes to mind for you?

5:40 | The silo-effect

9:41 | Getting a seat at the table

11:10 | Maximize data

14:00 | The richest source of customer feedback

20:20 | Product usage data

21:21 | Telemetry data + qualitative data = insights hiding in plain sight

22:08 | Rarely are you broken up with in the moment that the breakup happens

23:32 | Taking out the guesswork

24:36 | Structuring data in a consistent and repeatable way

26:30 | We're all on the same team

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0:00:08.8 S1: Hi, friends. Welcome back. I feel like it's been a million years since you’ve seen us on CS Operations. See, I don't even remember the title. A conversation with Emily Ryan. I'm Emily Ryan, and I'm so excited to have the opportunity as we have all year, this is actually coming up on a year of episodes, to have the opportunity to nerd out with some of my favorite people talking about one of my favorite topics, CS Strategy and Operations. This LinkedIn Live series aims to help define and defend investment in this critical organization, provide tips and tricks for designing a strategy to scale, and provide subject matter expertise to support this awesome new field. Each session will pose a different topic to a unique guest to help you get the most out of your time with us. If you have any questions or would like to connect with us or with each other, please feel free to engage via the comments. We'll try to leave some time to address questions during the session, but if we don't get to your question today, be on the lookout for future conversations, or you can visit our website to engage with our resources.

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0:01:11.0 S1: Let's change the way people work together. I'm so excited to have Joel Passen, and I actually didn't actually ask you how you say your last name, so my apologies.

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0:01:21.7 S1: Got it. You nailed it. Cool. We're good. Sweet. He is a SaaS entrepreneur, an investor, and an advisor. He is also the founder of Sturdy, a customer intelligence solution that empowers businesses to leverage unstructured customer feedback from every channel, like email, tickets, chats, meetings, and more. Sturdy uses AI and natural language processing to identify opportunities, reduce risks, and create more durable and profitable customer relationships at scale. I know durable is a huge word coming into the ecosystem right now, macro economy being what it is, but thank you, Joel, for joining me. Welcome. Thanks for having me. I'm glad to be here. Yeah, it's going to be fun. We always start with an icebreaker. Since we in the States, most states anyway, recently saw daylight savings end, it might be the last time that that happens depending on how things go in our government, but does daylight savings time mess with you? Relatedly, what is your favorite time of the day and why?

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0:02:38.6 S1: I'm going to answer this in reverse, actually. Favorite time of the day is I'm a morning person. I get to work out and it's quiet in my house. Also, I think I think a little bit better in the morning. This is probably good that this event is in the morning. Morning for me, I'm on the West Coast. In terms of daylight savings messing with me, it does because I have small roommates, a six-year-old roommate and an eight-year-old roommate. They don't necessarily understand, their biological clocks don't necessarily understand daylight savings. It messes with me because it messes with them. Right. Yeah. I have furry roommates who are also like, why have not my meals appeared yet?

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0:03:24.2 S2: Yeah. It's the same thing. Mine aren't that furry, but it's kind of same jam.

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0:03:30.5 S1: Yeah, exactly. Yeah. I'm also a morning person, which comes in handy these days. I'm actually doing an MBA at the same time. I wake up super early to do homework, which sounds really exciting. Well, you know how to live. I mean, that's amazing. Dream life. Yeah. Learning about applied financial management this term. Go me. Awesome. Well, the second question that we always ask is when I say customer success operations, what's the first thing that comes to mind for you?

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0:04:06.1 S1: I think about Rev Ops. I kind of blend all these things together. I'll tell you a quick story and the reason I mentioned this. First of all, I'll tell you, not to ingratiate you or the audience, but I think CS Ops is insanely important. I was at an event recently and somebody asked me a question like, if you're going to hire, you had $3 million in ARR and you had to assemble your team and turn in your budget. We were talking about budgeting. I'm like, what would be your, how would you backtrack the math? And I'm like, okay, first, first, good. You're talking about math. Math is good. And Ops sort of plays into that. And what I told them is I'm like, okay, I would try to figure out some sort of tech touch. Obviously I would have my head count resources. I'd be planning for some attrition in that. But I would talk a lot about in my budget, adding a CS operations or data analyst. And I would also, I think enablement CS enablement is also really, really important. And I think those are often overlooked as early hires. CS Ops, it's important to me.

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0:05:16.3 S2: I would hire that person really early in my life cycle.

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0:05:19.7 S1: Yeah. And, and to your rev ops point, right? I mean, we, we see sales ops come into play pretty early. We see marketing ops come into play reasonably early to get that scale, to get those touch points. But yeah, to your point, CS ops is just forgotten for a really long time.

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0:05:39.7 S1: I also think that there's a little bit of the silo effect too. I mean, you know, we're, we're talking about, we're talking about data that can impact, you know, CX CS and, you know, other folks, we're talking about customer generated data, right? And, and, and the topic of this, and I think it's really important to think about like, you can hire CS ops people. I would really want them tightly aligned to the rev ops org and the business or, you know, the business systems org. I think there are more and more product operations people coming up, but all of this layer needs to sort of funnel into a consistency. And I think that's one of the big opportunities that the industry has. And I think that CS ops people sometimes are on their own Island. And I don't necessarily think that's good because the data that they play with and the data that they're making sense out of can be used by all these other teams and the teams that are using, you know, conversely, like the rev ops and product ops, most people all need to use the same data sets to create new analytical frameworks.

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0:06:42.4 S2: So I'd like to see them less siloed. So I might say rev ops would be the first thing I think about because I think it's all kind of revenue operations. I think the next thing I'd say is like, keep them off their Island or out of a cave and put them more in the mainstream.

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0:06:57.5 S1: Yeah. Yeah. That's interesting. I don't know if it's an Island or a cave. I feel like, I feel like it might be an Island and it's like just far enough away that you can see other people. You can't actually talk to them. Yeah. They're kind of like, what does that person on that Island do over there? Oh, that's CS ops. I'm like, that's their little, why don't they have a lot of, there's not a lot of area on their Island.

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0:07:18.5 S1: You know, we need to also give them a nicer things. I think one of the things that I find in CS ops and you talk to them and these folks, these professionals, it's like, you know, how do you beg, but you know, what do you, how do you get resources? You know, do you have access to Tableau? Are you working or Domo or whatever you're using? And like, how do you, and they're kind of siloed. They're like, Oh, well I'm in our CSP and our CRM. Right. You're like, well, what, there's a broader subset of tools that in tooling that you should have. Well, we don't have the resources for that. Right. And it's the same thing by the way, in HR ops, I come out of the HR tech space, you hear it all the time, like TA ops, HR ops. They're kind of on their own little iceberg Islands in an orbit too. And they kind of look, their islands look and feel like the CS ops islands. Right. Yeah. Just like a person. And sometimes even part of a person.

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0:08:10.4 S2: Yeah. Yeah. They've got one or, and they're kind of like trying to find food.

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0:08:13.9 S1: Yeah. I, well, and the having nice things too. I think that that's, you know, that's one thing that, that we've spoken about a number of times on the Valuize side is as a CS operations person, learning how to speak in money because the value that you bring to your organization, if you can highlight that in real revenue and profitability terms, now folks are listening, but I think that it's taken a while for, for operations professionals to really get in that head space. Yeah.

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0:08:47.5 S1: I would, I would actually add to that and say, as a, maybe, you know, I've been sort of listening at, on the conference circuit for two years and attending stuff like this and like kind of showing up in the conversation and just to listen and absorb and learn more and more. I've been more of a CRO person. I've owned CS twice. And I've always had really been fortunate to have good chief customer officer, VP of CS, CX. I've always had customer operations by the way, early on operations really important. But to your point, like I think one of the things that I've heard a lot of on the circuit to, I think a greater extent, I heard it again this year, it was like, Oh, getting a seat at the table. And there was like, there's all this kind of like talk about getting to see the table. By the way, I've heard all of this in the HR Tech years and years ago, like how does HR get a seat at the table? I'm like, it's our most important asset to have our employees first. And you're like, yeah, it's really interesting how aligned these spaces are because HR talks about the employees getting, you know, if you talk to a CEO, like, and say, what's your most important asset?

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0:09:53.6 S2: They're like, Oh, our people. Right. And behind closed doors, they might say our cap structure, right. And I think in customer success, by the way, they're like, we want to see to the table and you go to the CEO, like what's your most important constituencies of people? And of course our employees and our customers, you know, they're equal. But yet I think one of the things that people forget about is thats lip service to a certain extent. And that if you want to see to the table, you got to talk in revenue speak because CROs and the product people, you know, the product people kind of get a pass to no fault of their own. They get, they get nice things. Sales teams get nice things because they own a huge number and somebody thinks they need nice things to make that number. But I think that's trend. We might be moving the needle for CS folks, but I think I really encourage people to think about like, yeah, when you speak in revenue, you wield power. That gets you a seat at the table immediately.

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0:10:49.2 S1: Exactly. Exactly. Yeah. So digging into like how, right? So, I mean, early in my software career, I had the opportunity to dive into customer success operations even before it really formally existed as such. And one of the first things I learned in that experience was to maximize data, to gain any level of consistency against delivery. Today, over a decade later, we still come across clients at Valuize who insist that they don't have data. And usually it's because they're focused on like product telemetry specifically, but I know Sturdy aims to debunk this myth by leaning into the rich data sets that commonly go unnoticed and aren't tapped into. What are your thoughts about how companies really, especially B2B, but you can talk about any company, can lean into that rich vein of data and what is this data and why is it important?

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0:11:52.7 S1: Well, I think it'd start with a stat. So we've had the really good fortune at Sturdy to analyze 55 million conversations collectively. And these can be things from call transcripts to tickets to in-app chats to customer email. And so when companies, to your point, when they talk about, oh, we just don't have any data or our data is a mess because you hear those two things in, oh, not quite ready for that because our data is a mess. And my answer to them is the first thing is I'm like, you have an enormous, enormous amount of language, like your language and feedback, and it's all stuck in email in a variety of silos. And so you have the data and you've been collecting the data for a very long time. And there's trends in that data. If you just think about the single channel in email, it's amazing. And yes, it is an enormous rat's nest of unstructured data. But the cool thing is there are companies and Sturdy is not the only one. There are others that are starting to make sense out of that and being able to distill that information from these silos, ingest it and restructure it in a very consistent, accurate way.

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0:13:01.2 S1: So the, we don't have any data is a tough one these days because you just have a ton of ticketing data. I mean, the backend of your Gong calls or your Zoom calls with your customers, enormously valuable data. So it might not be what you would think of your data, Emily, in the traditional sense, like how many emails have we sent? How many times have we engaged the customer, which is really important stuff, but we all, you know, that's the kind of stuff we probably should have, but that's all telemetry based numbers that we look at in our rears. So, yeah, there's by the way, you know how many, you know, I'll put you on the spot here. You might know the answer to this. I feel like I've switched the thing you're supposed to. It's a dialogue we can talk.

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0:13:46.7 S2: Okay, cool. If you don't, I have the answer. So if you don't want the answer, how many, so across B2B SaaS companies, $50 million in revenue or higher, how much do you think in terms of all the channels of communication with customers these days, minus in person, because that's impossible to track unless the calls are recorded. What do you think the richest source of feedback and customer feedback or insights is derived from what channel of communication?

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0:14:13.8 S1: Like where is it derived from today? Yeah.

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0:14:17.0 S2: Like what, where does the, where does the potential lie? What is the, what, where do you think the treasure trove is?

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0:14:23.1 S1: Gotcha. I would say it's just all of the back and forth engagement. So anytime your customer responds to something or reaches out, so I'm going to cheat and say any inbound email from your customer and more specifically support tickets.

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0:14:45.2 S2: Yeah. You're right. What we find is over 60% of the back and forth communication between customers and I'm talking users and customers, not just your key stakeholders, over 60% of it is an email across on an average nine inboxes. Yep. So if you think about it, yeah, we don't have the data. You can't say you don't have that. You have a lot of data just unfortunately stuck in all these little pits, these little tar pits that you can't get things out of, you know, conveniently. But there's a lot of really, you know, customers in an, you know, if you look at emails and you analyze emails, it's really like an unabridged, unbiased voice of the customer. They are telling you the answers. Yes. It's just really hard to get at.

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0:15:27.9 S1: Right. Well, and to your point, like the nine, I mean, there are nine silos into which those emails dump and there's overlap with different emails. So let me see. I would imagine that's, probably some of that goes directly to your sales person because there's never been a severed relationship there. Not that that relationship should be severed, but it should be re refocused. Right. It's your, whoever your technical kind of onboarding first person that talks to your customers inbox or group inbox, it's your CSM or CSM equivalent. So if you have a pool model or digital model, it's your support, ecosystem. So all of the tickets and all of the rich things there, I would imagine it's any engagement with your community or your marketing, ecosystem. Let's see, that's, that's half.

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0:16:23.3 S2: I'll give you, you're, you're an expert, so you're, but hold on, hold on. Yeah. Billing. Oh yes. So when you, when you build accounting, accounting is huge and it's, by the way, it's very literal. Those are very binary exchanges.

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0:16:38.2 S1: Can I have a copy of our contract?

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0:16:39.7 S1: When is the renewal of our contract? Yes. It's, it's November 15th. When's our auto renew trigger. Right. So, and possibly if you've got, you know, similar like that, linking that information with a support ticket, I'm cheating cause you told me this, but I'm linking that information with a support ticket that says, can you point me to the place where I can extract data from our system? Now you've got turn. Yeah. Well, you've got a couple of different vectors to say like, Hey, this is an issue. By the way, if you get a support ticket, it's kind of interesting for like when is our renewal date and somebody might go into Salesforce and be like, and by the way, in, in trying to do the right thing, you know, in a timely manner to provide an excellent level of service. Hey Emily, it's November 15th. Thank you so much. Is there anything I can help you with? No, there's not. Emily Ryan says, okay, case shut right. I'm moving on. If you blend all that language together or if those things get, escalated to somebody that's like, hold on a second, let's have a conversation with these people.

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0:17:42.0 S1: They're asking a question that, you know, 65% of the time leads to a cancellation in the next 12 months. Right. So we need to get this to somebody that has the aptitude, you know, probably an account manager or a CS person that can have a conversation with these people and better understand why they're asking the question. Can you save a couple of customers a year doing that? Yeah. That's using data hiding in plain sight to actually lift net retention or you're just kind of stem this, you know, stem off cancellations. Right. I mean, that's what we're talking about when we're using qualitative data to do this stuff. It's the, they're there, the, the signs, the insights are sitting on there. The other ones, by the way, so billing program management, people that are touching integrations or partnership types things where you're engaging or, you know, maybe you're upset, maybe you're getting upsold by account management. So, anywhere from like seven to 12, right. Seven to twelve touch points at any given time at larger enterprises. It's, it's vast, even bigger, could be marketing, could be advocacy groups, that actually get insights from customers where they're like, I can't give you a customer testimonial right now.

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0:18:47.6 S1: We'd love to, but we have this issue. So what does a customer marketing person do? Like they try to solve that. Do they escalate it? Do they have to do data entry somewhere? I mean, there's some really murky things that happen to even with people that are well-trained and have the best interests. Exactly. Exactly. Well, and you know, this is another reason why we, we try to help our, our clients with, with whatever system they have starting to make sure that you are viewing the customer with the same lens. We see a lot of folks silo internally, the customer's information and data set by internal team members. So my CSM has of you, my salesperson has of you, my technical account manager has of you and the views are slightly different. And so one of the things that we do is try to crack that open and even extend it. So not only is your whole post-sale customer team looking at the same set of customer information, but we're making sure that support has a view into that, that professional services has a view into that, et cetera. and it's, this is the same type of motion.

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0:19:53.9 S1: It's like these, not just the customer information, but the data around all of those interactions. And to your earlier point, it's not, it's not just about quantitative interactions. How many times or, you know, how many times did my customer user open or click on like that's important data probably, but then what, so what, what else happened?

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0:20:20.9 S1: I also think that product usage data, if you're in like the, we have payroll providers as customers, you don't just stop doing payroll or tail off and doing payroll in January 1st when your first payroll starts with your new customer. Yeah. Your usage goes from like a hundred percent to nothing that usage doesn't tell that story. So some of the things that we incorporate, I know that, Valuize does good work with customers around helping them sort of create, maybe more holistic health scoring. And I read a lot, you know, like part of the reason that I think I'm on this, with you today is like, you talked about data centricity and I was like, yeah, that's the, that's the, you know, and some of the value wise content, which I think is really quality. And I've mentioned this to your team before, really quality content for people that are looking to get information. You guys write a bunch of, but data centricity, which isn't a light reading topic, but it's really, really important. So, I mean, I think that's what you're trying to get at without beating your own drum, but it's really, really important.

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0:21:21.4 S2: So yes, telemetry data combined with qualitative data is sort of like, if you take the data that's been hiding in plain sight, the stuff that you're collecting, you have reams up, you just need to make actionable and you combine it with some of this telemetry based data. It accelerates or, or, I think enhances the story. Like you kind of get to the, what we're all looking for is like, this is happening. Oh my gosh. Like, and then you have to say why, and someone can be like, this is what we've seen. This is a lot of the topics in their conversations go around these feature requests. And one of the things we talk a lot about is like cancellations. They don't happen in a vacuum. No, it is a compilation of lots of things with all of these different actors in all these different silos. And this is part of the reason that's really hard to get in front of cancellations. It's like it's all over the place and it's death by a thousand cuts.

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0:22:08.5 S1: Yep. Yep. And just like any, any relationship, you know, rarely are you broken up with in the moment that the breakup happens, you've been broken up with mentally long ago, right? Yeah. Well, yeah, yes. And you know what we're talking about, we're talking about investing in relationships. So, I mean, that metaphor goes a really long way. And when I, I mean, I'm sure you get out and talk to your customers. I get out and talk to mine. It's like, yeah. I mean, think about your own relationships. There are fractures and fissures. And by the way, sometimes, and to this point, sometimes you have really hard conversations in a relationship, a personal relationship, and it makes you stronger with that person because you get to trust. And I think that confronting these fissures and fractures and client relationships head on and honestly with, with high intellectual honesty, right. Sometimes can create a really strong partnership with that customer. They're going to renewal infinitely, right? They love you. You've provided value. Listen to them and listening to their customers, like kind of how this all starts, because that's kind of key to a relationship. So absolutely. And you know, your, your, the work that you're, you and your team are doing helps enlighten your internal teams broadly about those things that your customer is telling you, right?

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0:23:26.3 S1: So you can have, it's, it takes the bias out of things or the guesswork like, Hey Emily, how is a XYZ customer?

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0:23:32.6 S1: Are they green, yellow or red? Well, I think, I feel like, I feel like that's how a lot of these conversations start where I'd like to say, I feel like they're a little bit of a yellow because we don't have this particular feature and the buyer, we lost our executive sponsor and the new executive sponsor has bought ADP or workday or whatever the competing product is before. So I think we have a little bit of risk. That's what I know. Now, if I'm a leader and I get that download and I'm like, okay, we need to fly to Topeka and go talk to these people and go, that's a trip that I'm going to budget. Like we need to get in front of these. What's their error? Oh, it's this. Yeah. We should get in front of those folks. Right. Yep. No, it's true. And I think, you know, one of the things that we were talking about the other day was just the kind of, you know, we talked about rev ops, the sales and marketing components of that really have learned how to get a lot of data and structure that data in a really consistent, repeatable way.

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0:24:36.2 S1: And customer success and post sales in general seem to continue to lag behind when it comes to truly understanding customers through data. Just as kind of our final moment here, what's your perspective when it comes to this like dichotomy within SaaS businesses and how can companies strive to overcome that?

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0:24:58.8 S1: So the first thing that I would say just with few words is that it's a maturity issue. You know, CS ops is relatively new compared to revenue operations, which is I think fairly stable. I mean, any business with over, I don't know, what do you think? I'm like $5 million in ARR? You're going to have a full-time rev ops person or at least somebody who's aspiring to do that role, if not augmented with some sort of consulting, cause you're going to need it. And as the motions grow, it just gets more, you know, those teams bloom. There's a whole industry around it. There are platforms, all kinds of stuff. It's mature. I think in CS it's just not mature. And I think the second point is, you know, how do we get people there? I think that like anything else, like how, how does a good sales person or account manager learn? They learn through osmosis through shadowing and being mentored by someone else. So I think CS ops and it comes full circle. Like you asked me what I think of CS ops. I'm like rev ops because I want my CS people, my CS team to be lockstep with the revenue motions with rev ops so that we have to pair them together and that'll increase the maturity on our mathy and data side of the world in customer success or post sales motions.

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0:26:10.9 S2: Yeah.

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0:26:11.4 S1: Yeah.

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0:26:11.7 S1: Like we have to, we have to get closer by the way. They're too, these teams are too segmented. They're too segmented. Like we're all in the revenue game and we're all in the delighting the customer game, whether it's product sales and customer success, we are all on the same team. And that I think sometimes is lost in fiefdom building. It really is. Yeah. Yep. That's a, that's a, that's a nice little tagline into, I will, I will shamelessly pitch our value experience framework release drives to break down those silos and help teams work together towards a common discipline so that, so that we're really driving towards net dollar retention and delight is a core part of that.

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0:26:53.4 S1: Well, let me know if you guys take the show on the road to Topeka. I'm available. They have planes. Sounds good. Sounds good. I feel like it's starting to get to the wrong season to be in Topeka, but It is actually, I've never been to Topeka this time of year, but I would imagine that we probably want to wait until the spring.

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0:27:11.4 S2: No offense to Topeka constituents, but yeah. Yeah.

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0:27:14.6 S2: I know that, you know, Kansas is a, is a neighbor of ours and man, do they get the weather that just comes right off, but skips Denver and then just goes straight to Kansas. So it's wait until spring. Well, as usual, this time just completely flew. Thank you, Joel. And thank you everyone for joining the discussion today to uncover data in your blind spots and maximize customer insights. Let's change the way people work together. Let's do this again too. Let's do it again.

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0:27:49.5 S1: Right. Let's, we have so much to talk about. We can keep talking later. Thank you. Thanks for having me. My pleasure.

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Customer attrition is a certainty for any SaaS company. The simple truth is that you will lose customers over time. Sometimes this attrition is out of your hands. However, at other times, you can directly impact whether or not it occurs. This post will explain customer attrition and how you can proactively mitigate it.

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What is customer attrition?

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Customer attrition is the natural process of customers leaving a company or product for one (or more) reasons. Customer attrition can be broken down into two categories: voluntary and involuntary attrition. 

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Voluntary attrition is when the customer chooses to end their subscription. A customer may decide to leave for various reasons. Perhaps that customer is dissatisfied with your service and has moved on to one of your competitors. Maybe they no longer require the service you're offering.

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Involuntary attrition is when a customer fails to make a payment, leading to their subscription being canceled. Maybe your customer has been acquired. Alternatively, your customer may have stopped paying bills and is on the verge of going out of business. Involuntary attrition is arguably more frustrating since there's little you can do to prevent it. For that reason, SaaS companies tend to focus on managing voluntary attrition.

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Whatever the reason, SaaS business leaders should not take customer attrition lightly. Why? Because customer attrition impacts the bottom line. According to Invesp, it can cost companies up to five times more to acquire a new customer than to retain an existing one. Moreover, Totango's Guy Nirpaz states, "70–95% of SaaS revenue comes from retention and expansion of existing customers." For SaaS companies, net dollar retention, or NDR, is the lifeblood of the business.

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NDR provides a revenue-based view of customer retention. NDR is increasingly important as you scale from small to medium-sized businesses and beyond. For example, a $5MM company that churns 20% can replace that $1MM with net new business when it's growing by +50% a year. But when a $30MM business needs to return $6MM due to customer attrition, this becomes significantly difficult if the growth rate slows.

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It should go without saying that improving customer attrition rates should be a top priority for SaaS business leaders. But how? Where should they start? Here are some tips.

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Six strategies to combat customer attrition

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1. Listen to customer feedback and act on it.

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Listening to your customers is more complicated than it sounds. And yet, customer feedback is the single most crucial element in reducing customer attrition. SaaS companies collect daily customer information but need to convert it into valuable, actionable data. Let's face it, NPS and CSAT scores hardly tell the whole story. Usage Data is excellent but can be misleading without context. Survey data is unreliable. That's where Customer Intelligence (CI) tools like Sturdy come in. Customer Intelligence is collecting and analyzing key customer-generated data to glean crucial insights such as risks, trends, and opportunities—all of which help drive revenue. CI is critical in unlocking customer insights, often using advanced data sciences like artificial intelligence, machine learning, and natural language processing. CI allows you to distill the insights from the noise so your team can take the following best action. Without CI, you're only half-listening to your customers.

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2. Make sure customers are getting value.

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One way to keep your customers is to make sure they understand the value they're getting. You can do this through effective onboarding and by setting expectations appropriately. Another way to ensure value is to go above and beyond expectations. This could mean offering extra features or benefits at no additional cost. It could mean providing superior customer service or simply doing whatever you can to ensure your customers are happy. By focusing on value and exceeding expectations, you can ensure your customers are satisfied with their purchase and more likely to stick around.

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3. Keep communication open and transparent.

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Keeping communication open and transparent with customers is essential for success. Through transparent communication, businesses can ensure customer attrition rates remain low, and customer satisfaction remains high. A ZDNet study concluded that "Organizations can reap rewards from being transparent. Nine out of 10 people (89%) said a business can regain their trust if it admits to a mistake and is transparent about the steps it will take to resolve the issue. A similar ratio (85%) are more likely to stick with them during crises." 

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4. Maintain product-market fit.

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Product-market fit describes your product's ability to deliver value to customers. If your customers stay with you and keep paying their subscriptions, you're in a good place. However, product-market fit is by no means the end of the line. It isn't a box you can check off and then forget about. To succeed, your product needs to continuously evolve to meet customers' needs.

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Early on, your top priority is feedback when you are working towards validating product-market fit. While collecting quantitative data like engagement metrics and survey scores is helpful, capturing your customer's unbiased, unabridged, and unsolicited voice is the most important thing to do. This is only possible with the help of a Customer Intelligence platform.

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5. Offer customer incentives and loyalty programs.

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We've previously explained why incentive programs are essential and provided five steps to build a successful one at your company. Customers loyal to a company are more likely to recommend the company to friends and family. Customer incentives and loyalty programs can be a great way to show customers that you appreciate their business. They can also be a great way to encourage customers to continue doing business with you.

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6. Provide a reliable and proactive customer experience

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Customer attrition is a "rearview mirror" metric. Traditional reports and surveys capture what's happened in the past. With Customer Intelligence, you have valuable insights at your fingertips to look forward through the "windshield" and to see around the corners along the way.

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Our research shows that 65% of accounts with an executive change churn within 12 months. Teams that act on executive change signals within the first 48 hours of discovery have a 33% higher likelihood of renewal.

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Using innovative data sciences like AI, ML, NLP, and deep learning, Sturdy analyzes every email, support ticket, chat, and more for specific insights. This empowers your teams to focus on relationships that drive revenue. 

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Conclusion

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By understanding customer attrition, SaaS business leaders can proactively address customer dissatisfaction before it becomes an issue and continually improve customer loyalty and value. This is a critical factor in any business's success, so it should be noticed. By listening to customer feedback and implementing strategies that keep customers engaged and satisfied, companies can ensure customer attrition rates remain low, and their bottom line remains healthy.

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Few things are more critical to the success of a business than developing customer advocates. Every brand wants to make its customers happy. In SaaS, even minor improvements in customer experience lead to more significant gains in customer retention. Considerable increases in customer engagement significantly boost retention revenue. According to Guy Nirpaz at Totango, “70–95% of SaaS revenue comes from retention and expansion of existing customers.” You heard that right — yes, this surpasses net new revenue figures. The initial sale generates as little as 5% of SaaS revenue. As the SaaS economy has matured, we have finally realized that replacing churn with new acquisitions becomes increasingly tricky, especially at scale. It suffices to say that in the world of SaaS, keeping your customers has become the top priority. 

What are customer incentives? 

According to our friends at Paddle, “Customer incentives are rewards granted to customers for engaging in behavior that helps build the brand.” Think of customer incentives as bonuses or rewards given to customers for promoting the brands. For example, customers may receive additional credits if they promote your brand on social media. Or, maybe you’ll give them access to more advanced features. They can be revenue related, such as making a repeat purchase or upgrading an existing plan. They can also be action-oriented, like writing a testimonial, referring another customer, or participating in a webinar or a panel. 

B2B rewards and incentives are diverse, including product discounts, rebates, discounted or free products, features, early access to new functionality, charitable donations, and more. Compared to their B2C counterparts, B2B businesses tend to develop more personalized, long-term customer relationships. Therefore, it’s common for a B2B incentive program to be more personalized and designed explicitly for specific customer segments and cohorts. For example, a discount incentive can be offered to a company that allows the vendor to use its logo for marketing rights. Or, the vendor can provide early access to a new SKU for free or at a discounted price. 

B2B incentive programs are generally less transactional than B2C programs. Instead, B2B programs are focused on deepening existing relationships. Customers must view the program as somewhat personalized, and it needs to offer a clear benefit. 

Finally, one of the best incentives for engaged customers is simply communication. As with any relationship, communication is critical. Companies that regularly communicate with their customers show that they care about them and want to keep them up-to-date on the latest news and developments. This communication can take many forms, such as emails, newsletters, social media posts, or even phone calls. By staying in touch with their customers, companies can build strong relationships that will last for years. 

Now that you have a good pulse on brand-building B2B behaviors and how to incentive those behaviors, here are five steps you can take to build a successful customer incentive program, grow customer loyalty, and improve customer retention. 

5 Steps to build a successful program:

There is no shortage of ways to incentivize customers to build your brand and business. Be creative. The sky’s the limit as far as creativity is concerned. However, not all SaaS companies are alike; thus, creating a custom incentive program is essential for success. It’s not like the worst that can happen is you don’t have any takers. A poor incentive program can lead to far worse outcomes — even compliance and legal challenges if you aren’t careful. Follow the steps below to ensure your program hits all the right notes.

1. Listen to your customers

Understanding your customers and ideal customer profiles (ICPs) is the first step to creating a successful incentive program. To better understand your ICPs, you must listen deeply to what your customers say. The key here is to go beyond traditional tactics like NPS and CSAT surveys like NPS and CSAT. Nothing speaks louder than the unabridged, unbiased, unsolicited voice of your customer. The most competitive businesses analyze everyday customer conversations to distill thousands of data points a month and capture customer sentiment at scale. Taking that step can only be accomplished through a customer intelligence platform (CIP). 

2. Choose your goals

As we mentioned above, there is no shortage of brand-building behaviors. Is your program focused on generating more customer testimonials and case studies? Do you need more customer references? Or is the primary goal to increase expansion revenue? Different goals mean different programs. They also represent different audiences. You’re not going to ask a struggling customer who’s still onboarding to write a case study. Similarly, you’re not going to incentivize a customer who’s already planning on expanding their seat count to upgrade. 

3. Choose a test group

Testing your program on a smaller group minimizes risk. There are a couple of ways to choose your test group. Let’s say your program aims to generate 10 case studies for a new website landing page. Traditionally this has been a pretty manual task. First, you reach out to your customer success and account managers to ask which customers are “happy” or “most likely to write a testimonial?” Once you’ve chatted with a few colleagues, you can pull together your group. Alternatively, you can aggregate customers exuding positive sentiment in seconds with modern technology like a CIP. You will save yourself hours of work and have the underpinnings of a repeatable process and, ultimately, a healthier incentive program.

But what if your goal is to incentivize customers to upgrade? How do you know which customers are already planning on expanding their account versus those who are just ‘happy’? Positive sentiment doesn’t always infer expansion opportunity. Don’t spend hundreds or even thousands of dollars building a program to encourage those customers who are already about to expand. Spend that bandwidth and budget on happy customers who need an extra nudge. The issue for most is that they cannot tell the difference. Without a CIP, it’s nearly impossible to differentiate between those two test groups. With a CIP, however, you can define and segment those customers who are ‘happy’ from those that are ready for ‘expansion.’ 

4. Build a budget

Regardless of your program goal, it’s essential to map out a budget. This process can be relatively straightforward if your incentive and desired customer behavior are tied directly to revenue. For example, if I’m offering $10,000 of free products to generate $15,000 of repeat purchases, that’s $5,000 of net profit. Budgeting gets more complicated as the incentives and behaviors move away from simple transactions. Let’s say your incentive to generate 10 case studies is to provide early access to a new product offering. Determining the monetary value of a case study is difficult. Understanding what you want to charge for a new product offering is difficult without going through the proper motions. Still, getting everything down on paper and setting up a rough projection is critical. Ultimately, your budget should be a tool that determines whether or not what you’re giving away is less than what you’re getting in return. 

5. Analyze and iterate

Rarely does a customer incentive program knock it out of the park on the first go. There are often many moving parts, and it can be tricky to nail the incentives down on the first attempt. Depending on what brand behavior you’re seeking, these programs can take weeks, if not months, to conclude. Requesting 10 case studies is not going to be a quick turnaround. That said, it’s essential to track your program’s progress effectively. There is no shortage of project management tools to choose from these days. A CIP lets you quickly track leading indicators based on sentiment and insights. Regardless of how you’re monitoring success, keep an eye out for improvement opportunities.

Conclusion

Engaged customers are essential to the success of any B2B SaaS business. To properly engage with your customers, you must listen to your customers at scale. Only then can you offer them the right incentives at the right time to boost brand-building behaviors. Pair that with excellent customer service and regular communication, and you can build strong relationships that will last for years.  

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In any business, customer churn—or the percentage of customers who stop using your product or service—is inevitable. Cancellations happen. But that doesn't mean you should just roll over and accept it. There are things you can do to decrease customer churn and protect what is arguably the most important aspect of revenue — the longtail subscription revenue of your customer accounts. Let's take a look at the seven most effective strategies for decreasing customer churn.

1. Understand the most common reasons for customer churn.

The first step in dealing with customer churn is to diagnose why the customer is canceling in the first place. There could be any number of reasons, but consistent themes and topics will emerge with the analysis. Churn doesn't happen in a vacuum. It's a culmination of bug reports, feature requests, executive changes, response lags, unhappy sentiment, contract requests, renewal inquiries, and more.

If your team receives one or two pieces of feedback from a customer expressing frustration, it might not be the beginning of the end of the relationship. But, what about 10 times in 30 days? What if that customer is still in the onboarding phase of their journey? You'd want to know. And, more importantly, you’d want to take action to repair that relationship. 

2. Elevate customer engagement early on. 

Speaking of the onboarding phase, the first few weeks and months after a customer signs up for your product or service are crucial. This is when they get to know your product and develop a relationship with your team. Most importantly, this is when customers determine whether your service is really going to drive the value outlined in the sales process. If you can increase engagement during this period—through things like work sessions, listening workshops, self-service content, regular check-ins, etc.—you can set your customers up for success and decrease the likelihood that they'll churn later on.

3. Listen deeply to what customers are saying.

As previously mentioned, certain insights can be indicative of future churn—things like executive changes, contract requests, questions about the contract terms, unhappy sentiment, etc. By listening for these insights, you can proactively identify opportunities to guide the relationship early on and take steps to prevent them from turning into bigger issues down the road. Traditionally, for most B2B SaaS enterprises, this process hasn’t been scalable. Listening to your customers at scale is nearly impossible. Luckily, new developments in AI and machine learning have enabled customer intelligence platforms (CIPs) to analyze every email, support ticket, chat, and more for specific insights that empower your teams to focus on relationships that drive revenue. 

4. Take action on customer feedback quickly. 

If customers feel like their voices are being heard and that their feedback is being acted on, they're much more likely to stick around. As a baseline, make sure you have a system in place for collecting customer feedback (surveys, Net Promoter Score® emails, etc.) and that you're regularly reviewing that feedback to see what changes you can make to improve the customer experience. Additionally, take a step beyond soliciting feedback to ensure you’re capturing customer sentiment at scale. Nothing is more powerful than the unabridged, unbiased voice of the customer. As we mentioned earlier, that can only be accomplished at scale through a customer intelligence platform.  

5. Understand what features and services your customers want most. 

Find ways to add value for your customers—through things like upselling, cross-selling, or simply offering new features or services—you can reduce the likelihood of them canceling their subscription. At the end of the day, customers are either growing with you or away from you. Identifying trends in what your collective customer base asks for the most is a surefire way to keep your customers growing with you. Customers who feel like they're getting more bang for their buck are less likely to look elsewhere for a similar product or service staving off painful losses to competition.  

6. Provide the level of service you’d expect as a customer.

One of the best ways to prevent customers from churning is to provide them with the level of customer service you’d expect as a customer. If your customers feel like you're listening to their concerns, issues, and suggestions and that there is some actionable output, they’ll be less likely to look to a competing product that can provide them with what they need. The root of excellent customer service starts with simply listening and taking the next best action. Adopt the mantra of listening, acting, and improving. 

7. Seek to develop advocates, not just keep customers. 

Customer-obsessed companies don’t just service customers, their goal is to create advocates. Customer advocates are the ultimate customers. They serve as references and speak at industry events and webinars. They provide success stories, product reviews, and quotes for your marketing team. Developing advocates is about putting your customer first, and putting your customer first starts with listening at scale. It’s high time to start using all the feedback your customers give you daily to better understand their wants, needs, and issues so you and your teams can take the necessary action.  

For most B2B SaaS companies, customer churn is what we call the CODB — the cost of doing business. But the fact of the matter is that churn is a “rearview mirror” metric. Traditional telemetry-based reports and customer health scorecards capture what’s happened in the past, and most of the time, if you’re dealing with churn, you’re already too late. With CIPs, like Sturdy, you have valuable insights at your fingertips to look forward through the “windshield” and to see around the corners along the way. This allows you to detect and combat churn before it happens. It’s like a lead-gen for building more durable, profitable relationships. 

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Churn. We've all heard about it before, especially if you're building a SaaS business. There's no shortage of thought leaders who proclaim the all too simplistic mantra: "Decrease churn! And increase profits!"

Yet, for many, churn as a metric is confusing and ambiguous. For example, customer churn is different than revenue churn for example and there many ways to calculate churn leading to confusion across your company.

If you're tired of the over-reliance on churn, you're not alone. Analysts and investors have been increasingly skeptical of churn rate calculations for years.

“There are too many darn ways to calculate churn. That makes it ambiguous.” says, Dave Kellogg

So if churn isn't the magic pill many businesses want it to be, what should you be looking at?

It all starts with, net dollar retention.

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What is net dollar retention (NDR)? 

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Net dollar retention (NDR) aka net revenue retention (NRR) has emerged as one of the top SaaS metrics that matter and for good reason.

NDR takes into account upgrades, downgrades, and churn to quantify how much recurring revenue from current customers you retained across a defined period of time. Why focus on a single metric such as churn, that doesn't actually give you the complete picture of the health of your business?

While no one metric is going to transform your business overnight, net dollar retention does help answer two incredibly important questions for businesses (especially SaaS businesses) looking to grow.

Net dollar retention can help answer:

1. Is your product delivering the value promised during the sale? 
2. Are your customers growing with you or without you? 

Having answers to these two questions can dramatically improve your business across the board.

What makes net retention so powerful is that for most companies, it’s cheaper to sell to existing customers than to sell to new customers. This makes net retention the most cost-efficient way to accelerate revenue growth. Instead of investing tens of thousands of dollars in a new marketing campaign, you can strategically use net dollar retention to improve the qualities and services of customers who have already trusted you enough to make a purchase. Yes, acquiring new customers is part of the business game, but all too often businesses neglect one of the most important revenue streams that already exist: current customers.

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How to calculate net dollar retention

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If your NDR is over 100%, this means that an increase in revenue is attributable to your existing customers.

Here’s how to calculate NDR. 

(Starting MRR + expansion — downgrades — churn) / Starting MRR  = NDR

Here’s an example.

Let’s say you start the month at $100,000 in recurring revenue (MRR). Over the month it added $25,00 in expansion revenue, has $10,000 in downgrades and another $5000 in churn.

($100,000 + $25,000 — $10,000 — $5000)/$100,000 = 110% NDR.

Your MRR is $110,000 with an NDR of 110% This is good. Essentially, your upgrades / upsells lifted your revenue despite losses. 

Without understanding your net dollar retention rate, you might be under the impression your business is sinking without a solution in sight. But with the knowledge that current customers are helping keep your business afloat, you can continue to invest in your marketing and business strategy without making rash business decisions.

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What is a good net dollar retention (NDR) rate?

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A minimum NDR rate of 100% is considered good for SaaS businesses selling to the SMB market. Selling to smaller accounts naturally yields a lower NDR. SMB clients are less financially stable, ripe for acquisition, and have smaller budgets. 

An excellent enterprise NDR rate is 130%. As with many SaaS metrics, there are other things to consider. For example, Workday’s NDR is 100% but gross retention is 95%. Either Workday is very good at selling the “whole” deal or their product footprint presents limitations. 

Here are some examples of net dollar retention rates for some interesting SaaS and SaaS-enabled companies.  

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Why you need to care about net dollar retention.

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NDR provides a revenue-based view of customer retention. NDR is increasingly important as you scale from a small to a medium-sized business and beyond. For example, a $5MM business that churns 20% can replace that $1MM with a net new business when it’s growing by +50% a year. But when a $30MM business needs to replace $6MM this becomes insurmountable especially if the growth rate is slowing. Understanding net dollar retention from the start will allow you to stay the course if your NDR rate is in line with or above average. Similarly, a low NDR score means you may have bigger challenges within your business you need to address before further investing in scale.

As with most things in business, the effects of NDR compound with time. It’s either additive or punitive with every customer that you acquire. This means that small upticks in NDR can add up to very large differences in total revenue over multiple years. For example, assume a business had $10MM in revenue last year and consistently generates 20% of revenue from new customers. Improving the  NDR from 95% to 105% may sound meager, but over five years the business will gain another $5MM in revenue. 

One of the biggest challenges within a business is knowing those small actions that have life-sized effects. Monitoring and tracking your NDR rate is invaluable in helping you build a sustainable business over the long term.

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How to increase your Net Dollar Retention.

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Net dollar retention is an important metric to track. So the question is... how can you start identifying those opportunities to grow and deliverable value at scale?

First, hire a great team of CSMs who know your customer's needs and pain points inside and out.

Second, develop more premium services to sell to your customer base.

While on paper, this sounds straightforward and doable. But frankly, this takes a lot of time, resources, and buy-in from management to create enduring impact. 

Now consider this.

What if you had a “tool” that could analyze customer emails, tickets, and conversations for important signals that are typically related to predicting churn?

Maybe something that could listen for suggestions about features and products that might accelerate value capture and lift revenue.

What if you could start such initiatives without major upfront investments in data infrastructure or change the way your teams work?

We may be biased, but here at Sturdy, we created that exact tool. Connect with a member of our team to learn how tracking NDR and other critical metrics can help take your business to the next level. 

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Despite customer intelligence still being an emerging field, there are already many incredible CI platforms that can help you get the most out of your data. Utilizing customer intelligence data will not only help improve your overall business strategy, but it’s also a powerful way to improve customer satisfaction and customer experience. 

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Data on its own isn’t beneficial. What matters is understanding the customer journey of your users and analyzing data, customer feedback, and customer behavior to make better decisions.

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But as with most things in business, not all customer intelligence platforms are created equal. Depending on your goals, the size of your company, and your budget, each platform has its own strengths and weaknesses.

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Whether you’re already sold on the value of customer intelligence or looking for ways to take your business to the next level, this article will cover everything you need to know about choosing the right customer intelligence platform for your needs. 

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Choose a customer intelligence platform that works well with your tech stack.

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Businesses today, on average, use 37+ tools across their teams and departments. Every department has its “go-to” tools. Yet, keeping track of all that data collected by these tools can take time, and it only gets more challenging the more systems your business uses. With so many silos, it can be impossible to understand all your data in aggregate.

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When choosing a customer intelligence platform, the platform you select must integrate deeply with the critical components of your current GTM tech stack.

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For example, at Sturdy, many of our customers use Salesforce, so we began focusing on Salesforce integrations for our customers who rely on using the most popular CRM in the world. A customer intelligence platform can have flashy dashboards. Still, it will be challenging to realize game-changing value if it doesn’t pull the full payload from your CRM. 

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At a minimum, buyers must choose a system that integrates directly into your CRM, email, and ticketing system. Be skeptical of CI tools that claim to integrate with hundreds of tools “out of the box.” Chances are these systems are using a third-party integration platform. While third-party integration platforms are great for some things, they can be limited when ingesting data from custom fields. And otherwise, they represent another failure point on the reliability daisy chain. 

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Many CI platforms, such as our platform, Sturdy, become more valuable with more data they access. To that end, it’s essential to identify your largest customer feedback channels. For most of us, it’s likely email. Our research has shown that over 60% of B2B customer-to-business conversations are over email. This makes a tight integration with your email platform imperative. The right CI tools analyze email, and then and only then can they give you predictive customer intelligence data based on the bulk of your everyday customer interactions.

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Pro tip: When considering customer intelligence platforms, integrations matter. Choose a system that has authorized integrations with your other vendors’ marketplaces. Avoid systems that rely on third-party integration platforms. And, if email isn’t a core integration, you’ll likely be missing the lion’s share of insights about your customer relationships. 

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A secure, privacy-first customer intelligence platform

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Let’s face it, there’s a consummate conflict of interest in businesses today. Business units must leverage data to turn raw information into actionable insights. On the other hand, InfoSec and privacy teams must ensure compliance with a myriad of regulations relating to collecting and using data, mainly when it contains PII.  

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Personally identifiable information or PII is any information that permits an individual’s identity to be directly or indirectly inferred, including any information linked or linkable to that individual. But, if you collect someone’s name and email address, you are collecting PII. For this reason, you must choose a CI platform designed for the privacy-first era. Anything less is asking for trouble. Here are some tips to get started:

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First, ensure your potential partner maintains an information security program certified by yearly SOC2 Type II audits. This protects the security, availability, confidentiality, integrity, and privacy of their services and your customer data.

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Next, understand each provider’s approach to processing PII. Being SOC 2 Type II isn’t really about privacy. Otherwise, it’s essential to know if a vendor’s employees, consultants, or sub-processors have access to your customers’ PII. If they do, this is a problem. Look for a solution that offers a virtual data clean room. This way, you can ensure that data from different systems, including email, ticketing, and customer relationship management (CRM), is securely funneling into one spot. This data is encrypted and then anonymized, making it impossible for anyone in the data clean room to access PII. 

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Choose a customer intelligence tool that gets buy-in across all your teams. 

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There are very few teams in a SaaS business that don’t need more insights about customers. Customer intelligence is something your entire company should be involved in. Everyone in your organization will benefit from your chosen customer intelligence platform, from engineering to product to marketing. 

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When choosing a CI platform, consider the following:

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• Insights for various teams: Customer Intelligence isn’t just for customer success teams. Product and engineering teams can immediately benefit from learning more about customer frustration, confusion, and wants directly from the voice of the customer. Marketing teams can transform positive insights into customer references. Rev Ops and the BI team can create new analytical frameworks from previously unavailable data.   

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• Fast time to value: Let’s face it, we’ve all bought platforms that were oversold, hard to implement, and even harder to administer. Look for solutions that can deliver insights to your specific use cases quickly. Understand the resources required to start receiving value and what resources are needed to maintain the program in the future. 

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• Tech stack: When choosing a customer intelligence platform, the platform you select must integrate deeply with the critical components of your current GTM tech stack. And don’t forget email. 60% of customer-to-business communications start with an email. 

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• Avoid duplicate functionality: CI platforms often have similar functionality to systems you already have, like customer success platforms and CRM systems. Look to compliment your existing system with rich data from a customer intelligence solution. 

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• Security: Does the platform have a clear and transparent take on data security? Ensure that any system you choose is SOC 2 Type II ready.

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• Data privacy: How does the platform handle data privacy? Is the vendor using anonymization, pseudonymization, and de-identification techniques?

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Customer intelligence is not a magic bullet: Avoid platforms that make incredibly bold claims.

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It’s essential to have realistic expectations when choosing a CI tool. Just as AI-driven content marketing can be helpful for copywriting and content marketing, it won’t do all the legwork for you.

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This advice applies to customer intelligence platforms and any SaaS tool your business might use. Many “all in one” tools or “magic bullet” solutions claim they can do everything. But remember, the more the vendor tries to do, the more likely they, too, have “soft spots” where the technology isn’t good. 

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At the end of the day, a customer intelligence solution should help you operationalize your practices and programs and get your entire organization enthusiastic about using insights to improve products, drive growth and expansion, and, ultimately, increase your NDR. Find solutions that demonstrate a clear path to value in the shortest time. These are the solutions that the C-suite can fund. 

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Finally, customer intelligence is a hot topic. But it’s not exactly new. So with the tremendous growth in the CI world, some organizations have failed with products that don’t deliver value. The good news is that integrations, data sciences, and privacy tooling have all dramatically improved in the past 3-5 years. This has made products more powerful and easier to maintain.

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Turn customer feedback into actionable insights. Get clear on your CI goals.

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Customer intelligence tools continue to innovate incredibly quickly, but choosing a tool that serves your specific needs will make or break your experience. 

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Perhaps you’re really focused on reducing churn. You may want a platform that streamlines your data points in one easy-to-read channel. Improving your customer experience is your number one goal. Increasing customer lifetime value, for example, is a common goal regarding competitive intelligence.

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Of course, you’re almost certainly going to have multiple business goals. Still, it’s critical to have a clear idea of what you’re hoping the CI platform can help you accomplish from the start. Before you schedule a demo or request more information, have 2-3 specific goals in mind. 

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Invest in both the now and the future with customer intelligence

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There are significant gaps between what customers think about your products, the level of services you provide, and the execution of the journey you’ve outlined. The question is, “how seriously are you taking their feedback”? How closely are you listening to your customers? Churn doesn’t happen in a vacuum. It’s a culmination of feature requests, “how to” questions, executive changes, response lags, unhappy sentiment, and more. The right customer intelligence must deliver the insights to help teams create more enduring relationships with arguably the most significant cohort of humans outside your employees — your customers. 

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While customer intelligence 2.0 is still in its infancy, businesses that utilize modern CI solutions effectively have a clear competitive advantage over those that do not. Nothing speaks louder than the voice of your customer. Today’s customer-obsessed teams make better decisions based on insights into the data customers generate for us with every conversation.

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Interested in seeing around the corners? Learn where customer intelligence is going. Schedule a demo with Sturdy today.

In today's increasingly competitive business landscape, you and your team need every possible advantage to help you stand out.

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From analyzing customer data to perfecting the customer journey for your users, there's no shortage of things to do to give yourself an edge. Today's most successful businesses continue to turn to customer intelligence to help them improve their products and services and to implement an effective business strategy.

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In this article, we'll answer the question: What is customer intelligence? As well as show how customer intelligence can be instrumental in improving customer loyalty, customer experience, and more.

What is Customer intelligence?

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Customer intelligence is the process of collecting and analyzing customer data from internal and external sources. It plays a critical role in unlocking customer insights. 

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Customer intelligence (CI) covers everything from interviewing your customers and asking for direct feedback to looking at your data to know where there's room to optimize your funnel. 

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The customer intelligence process is not something you can check off your to-do list and call it a day. Instead, it's a never-ending process that will keep you competitive. 

How to turn customer data into actionable insights

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When a customer emails you, "Hey, can you add this feature?" they want you to use that data. In a perfect world, you could implement any feature a customer requests. Still, as you likely know all too well, resources are limited.

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To make matters worse, ensuring the data gets to the right team can take time and effort. Collecting data is easy, but turning that data into insights is the challenge. Unstructured data is one of the biggest challenges teams face today.

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It's not like your email messages have a data field that tells your engineering team, "Hey, build this." 

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At smaller companies, you can get by manually recording this information with rules like "Hey, if something important happens, log it in Salesforce." 

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At larger companies, this doesn't work. 

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The conversion of unstructured data to useful data is the most challenging part and where you can reap the most significant benefits. Turning unstructured data into helpful info is one of the most critical parts of a successful customer intelligence strategy.

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Today, many organizations are getting hundreds, if not thousands, of messages daily. And virtually none of that data is converted to easy-to-use insights automatically. 

Cracking the customer intelligence code: "turn noise into music."

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Imagine if all of the customer data across your company was working together (including your black holes, like email). Imagine the efficiency your organization can achieve when you're not only collecting relevant data but you know exactly what steps you and your team need to take. 

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This is customer intelligence at its finest.

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If your best customer posts a bug, it might not be a big deal. If your best customer complains about a bug in chat, email, and ticket system, well, someone better take a look.

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Before the emergence of customer intelligence platforms, this type of identification and triage was almost impossible, which is one of the biggest reasons we created Sturdy.

Analyzing customer data to win big with your business

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We should continue doing everything possible to mine our customer communications and develop strategic customer signals. Yet, many companies know more about their anonymous website visitors than their paying customers. 

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Truly understanding the customer journey of your customers from start to finish can pay massive dividends down the line. Understanding customer behavior and customer signals and being proactive in finding your users' pain points can dramatically improve the health of your business.

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Virtually every company has a way to track and monitor its website visitors—something we like to call table stakes. Yet, almost zero have any way to monitor and monetize the happiness of their actual customers.

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Here's a challenge...

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Answer this: If your company was about to lose a customer, who would be the best person to save that customer? What metrics would you use to support your answer?

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Most companies need customer intelligence data to answer this question.

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Let's take it one step further.

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How many times did a customer say, "You guys are great!" last month? How many times were those happy customers converted to references and case studies? And how many of those references are delivered to your sales team to help them close new business?

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Again, it's the 21st century.

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We realize the challenges of customer intelligence are great.

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But in this area, failure is unacceptable. To have a truly operationalized customer-focused company, you need to mine these communications without bias and without manual data entry.

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You need something that never gets tired, doesn't need training, and gets better as you throw more data at it. And most importantly, you can't wait until the quarterly business review is complete with triaging a churning customer.

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Customer intelligence solutions are the answer to staying relevant in today's business world. And here at Sturdy, we are on a mission to help businesses deliver better products, services, and experiences through actionable data.  

It's official: Announcing our SOC 2 Type II Report

Shortly after launching Sturdy, we started our SOC 2 certification process. A SOC 2 report is for services organizations that hold, store, or process the information of their users. You can read more about it here.

Late last year, we obtained our SOC 2 Type I report. This represents a "snapshot", indicating that we have robust controls in place to ensure the security and availability of our customers' data.

Today, we are announcing that Sturdy has obtained a SOC 2 Type II report. This is the most comprehensive SOC protocol, and attests not only to the suitability of our process and systems, but our operational effectiveness of sticking to those controls over a period of time.

The full writeup describes our suite of controls for securing and handling customer data, including:

• System monitoring and ongoing risk assessments
• Internal access control to production environments
• Disaster recovery, data backup, and incident response processes
• Communication of changes to customers
• Employee on-boarding and termination processes

We're proud of this report. It is a reflection of our dedication to security and the product of many months of hard work from our team, particularly Eric Weidner. Our commitment to security is about more than checking a box: every day we make sure that our systems and processes are worthy of the important data our customers trust us with.

Sturdy is a data-centric system of intelligence for post-sales teams. Working with data, including some of our customer's most sensitive information is what we do. We work to earn their trust by putting security and privacy front-and-center. This includes industry-leading controls, data minimization, and a secure-by-design architecture. Perhaps most importantly, we have built a security-conscious culture from Day 1: everyone at Sturdy knows that we solve for security first. You can read more about our processes and approach below.

Security Program

At SturdyAI, the security and integrity of our customer's information is of utmost importance. Therefore, Sturdy has developed and maintains a comprehensive Information Security Management program to manage risks to the security, availability, confidentiality, integrity, and privacy of Sturdy systems and products. Our program has been independently audited and certified to meet the requirements of Trust Services Criteria SOC2 Type II.

Privacy

Sturdy products utilize customer communication data to detect important signals that may have private information included such as names and contact information. To protect the privacy of this information, we maintain policies and processes to comply with data privacy regulations such as CCPA and GDPR and to help our customers comply with their obligations as the controllers of this data. Please see the Sturdy privacy policy for more information on data privacy practices and controls.

Infrastructure

Sturdy utilizes Amazon Web Services (AWS) as the Infrastructure-as-a-Service hosting provider. All data stored in AWS data centers located in the United States. Communications into our services are encrypted-in-transit and data is stored encrypted-at-rest using industry standard encryption mechanisms. Web application firewalls and network management tools such as VPC's, private subnets, and security groups are used to manage the flow of information and access between services. Infrastructure services are defined, managed, and deployed with Infrastructure-as-Code orchestration tools for consistent and repeatable systems.Tenant data is isolated in separate systems and production systems are kept in restricted access accounts separated from the development environments. 3rd-party penetration testing is conducted annually.

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Questions about Sturdy's security program? Contact us at security @ sturdy.ai. 

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We are excited to announce that we’ve raised $3.1M in a financing round led by Lawson DeVries at Grotech Ventures. We'd also like to welcome Lawson to the board of directors. He brings over 20 years of software-focused venture investing and management experience with him.

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Read the full press release here.

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The idea for SturdyAI came from running SaaS businesses for the past 15+ years.

Our “Aha!” moment was when we realized that our customers were actually telling us what they want and need, every day.

The idea for SturdyAI came from building, bootstrapping, and scaling successful SaaS businesses. While running companies we realized that there is an ever-growing body of valuable data being created by our users. This feedback is just sitting in email accounts, in video conferencing systems, in chat logs, and buried in ticketing systems. We founded SturdyAI to empower businesses to solve problems that we faced as entrepreneurs and executives. At the end of the day, running a SaaS company is about keeping customers and taking advantage of the long tail of subscription revenue.

With the subscription business model reaching near ubiquity in many industries, particularly in cloud-based software, driving dollar retention (NDR) has evolved as the most important business metric. Companies with higher dollar retention are simply healthier and more valuable. So how does a subscription-based business drive dollar retention? Our earliest decks talked about, “getting your data in one spot”. But that wasn’t the problem we were trying to solve (wanting to see all the data in one spot is a symptom, not a solution). The problem wasn’t really a communication problem, it was a mining and refining problem. The problem we solve is separating the signals form the noise.

When a customer requests a copy of her contract, that message must get forwarded to the "Saves Team" - immediately. Save a customer — improve NDR.

Customers give us information to run our businesses better, to predict churn, to capture references, to get in front of renewals, to prioritize features, yet these critical signals are trapped and decaying in dozens, if not hundreds of data silos. Our customers are giving us the "answer to the test" in Slack, Email, Zendesk, Salesforce, Gong, Zoom, etc. Today, the only way we utilize this information is if someone manually identifies, records and escalates it.

These signals are immensely valuable. For example, reducing churn from 10% to 9% in a $10 million ARR business means that every customer is worth $17k more in lifetime value. And reducing churn in this example is just saving 5 customers.

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Today's CX stack is missing a systems of intelligence. Sturdy fills the void.

Greylock's Jerry Chan may have coined the term system of intelligence. He wrote about the category in 2017 saying that "What makes a system of intelligence valuable is that it typically crosses multiple data sets, multiple systems of record." He actually predicted that SturdyAI would exist — "The next generation of enterprise products will use different artificial intelligence (AI) techniques to build systems of intelligence."

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SturdyAI’s customer intelligence and automation solution empowers B2B SaaS companies and other subscription-based businesses to: 

• Unify all sources of customer feedback like email, tickets, chats, call transcripts, surveys, and more, into a unified channel.
• Analyze all customer feedback for important business insights like churn triggers, contract requests, buyer changes, feature requests, quality of service issues, and more that help lift dollar retention (and more).
• Create just-in-time automations to drive insights to the people, teams, and systems that need them most to enable immediate actions.

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We're just getting started.

We aren’t here to reinvent and change the way teams or companies work — necessarily. And that is what is so exciting about what we do. SturdyAI is the force multiplier for your business. If you already have a cutting edge BI tool, we just give it better data. If you have a good CX app, we make it more insightful. If you have spent years perfecting your customer health score, we have a new data source to make it more accurate. If you have a great Customer Success, Account Management, Operations, Marketing, and Product teams, we make them more efficient and provide them with better data.

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“SaaS companies collect a ton of information from their customers every day, but much of it fails to convert to useful and actionable data. Now using AI and automations businesses can proactively understand whether their customers are likely to churn, which features will entice them to renew, are they experiencing bugs, are they happy or not, and much more.,” said Lawson DeVries, Managing General Partner, Grotech Ventures. “Customer retention and expansion are critical for SaaS businesses to maintain consistent growth trajectories, especially as we head into a more challenging environment for acquiring net new customers. Actionable customer intelligence is no longer a nice-to-have aspect for companies of all sizes – it is mission critical for businesses to thrive in today’s market. Grotech has a long history in this segment of the software market, and we are proud to be a catalyst to help fuel Sturdy’s continued strong growth and bring AI to companies that will need to do more with less now and in the future,” continued Mr. DeVries.

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“Churn doesn't happen in a vacuum. It's a culmination of bug reports, feature requests, executive changes, response lags, unhappy sentiment, and more. Sturdy discovers the preemptive signals that help teams create more enduring relationships to lift dollar retention.,” said Steve Hazelton, CEO and co-founder of SturdyAI.

“Every SaaS company has a customer database of record, some have systems of action like customer success platforms but the critical component that most companies lack is a scalable system of intelligence — a system that listens to all of your customer feedback and routes the important things to the right people in the systems that they use every day. That is why we built Sturdy.”

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Interested in learning more about SturdyAI? Get in touch.

‍Churn hurts

No matter how great your sales machine is at acquiring new customers, unwanted customer churn creates a real drag on exponential growth. If you’re not thinking about churn, you’re probably living in your own fantasy world with dragons, fairies, and other magical creatures.

SaaS companies are banking on their subscription revenue compounding. This is only feasible if you hold onto your base. What’s more: without the base, you can’t upsell and expand.

How closely are you listening to customer feedback?

Search the internet for ways to prevent churn and you’ll find all kinds of good advice. But what if we told you that your customers are sending you signals every day that have potential impact on top line revenue?

Listening to customers is harder than it sounds.

Customer-facing teams at unicorns interact with thousands of customer accounts and tens of thousands of users every month via email, chat, support tickets, video conferences, surveys, and more. Nearly 20% of of this "feedback" contains valuable insights that teams can use to improve products, strengthen relationships, and reduce churn. 

You're invited

Join Joel Passen, 3x CRO and co-founder of Sturdy.ai, for a live 30 minute product talk to see how innovative customer-facing teams are leveraging AI-based solutions to better understand, improve, and expand customer relationships — and battle churn! 

Jun 7, 2022 11:00 AM Pacific Time (US and Canada)

Register here

During this product talk you’ll learn:

• How post-sales teams are leveraging data that has previously been hiding in plain sight to detect potential churn
• How CX teams can easily deploy AI-based technology to gain new insights on how to deliver value to their customers
• How product, customer advocacy, and leadership teams can access unbiased customer insights

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Teams at payroll companies interact with hundreds of customers every month via email, chat, support tickets, video calls, surveys, etc. Everyday customer conversations create an enormous and ultra-valuable data set.

On average, a $10m payroll company produces more than 10,000 customer conversations every month. Nearly 20% of those conversations contain valuable information that teams can use to improve client retention rates and improve the overall customer experience.
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Stay competitive. Start improving processes, relationships, and revenue by using the most valuable data you already have: the conversations you’re having every day with customers.

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Save your spot. Register now.

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Join Sturdy’s CRO, Joel Passen (Paycor, Newton), for 30 minutes to see how innovative teams are leveraging customer conversations to impact the top line with Sturdy’s modern customer intelligence solution to:

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- Gather valuable customer intelligence at scale
- Automate repetitive, inefficient, yet critical tasks
- Turn the voice of the customer into actionable outcomes

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Live Workshop: Leveraging Language for Customer Intelligence

Apr 28, 2022 10:00 AM Pacific  / 1:00 PM Eastern

Register here

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In B2B SaaS businesses, customer-facing teams interact with hundreds of customers every month via email, chat, support tickets, and video calls. Nearly 20% of those conversations contain valuable information that teams can use to improve products, strengthen relationships, and impact revenue. For subscription-based businesses, the insights that can be derived from a company’s language cube makes accessing this data a business imperative for leaders.

Customer language is an enormous data set. On average, a $30m B2B SaaS company produces more than 10,000 customer conversations every month. How closely are you listening?

Capturing, consolidating, and analyzing the true voice of the customer sounds like a good idea, right? To be a successful customer leader, your teams must be able to devise new ways to improve the overall customer experience and, at the same time, drive value. With a plan in place to use customer language — the authentic voice of the customer — your line of business is empowered to influence customer engagement through better product inputs, build deeper relationships with multiple stakeholders, and drive revenue retention and net dollar retention.

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You and your team are invited! 

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Join Cynthia Beldner, Customer Success and Operations leader, for 30 minutes to see how innovative teams are leveraging customer conversations to:

- Gather valuable customer intelligence at scale

- Automate repetitive, inefficient yet critical tasks

- Turn the voice of the customer into data any team can access and use

This is also a great opportunity to see Sturdy in action.

Save your spot. Register now.

Sturdy’s two-way Salesforce integration makes customer insights – about products, processes, relationships, and revenue – actionable, trackable and reportable in any object in Salesforce.

Sturdy is a customer intelligence solution trusted by leading CX, product, and operations leadership at some of the most innovative B2B SaaS companies.

This easy to implement solution is designed to help businesses and organizations improve their products, processes, relationships, and revenue by using the most valuable data they already have: the conversations they’re having every day. Not only can Sturdy detect signals in conversations — in real time — accurately, now it can automatically push critical signals and insights to systems and humans that need this critical information the most - no coding required. 

How to Enable Your Team

Sturdy detects events and insights– executive change, expansion opportunities, unhappiness – from your emails, call transcripts, chats, support tickets (wherever customers are talking with you)! The full details of the conversation and signals detected are sent to Salesforce. A new Signal Event is assigned to the right team member and, of course, recorded to enhance reporting, health scores, etc.

See for Yourself

Interested to learn more about how the Sturdy Customer Intelligence Platform can help empower your teams with the insights to build better products, relationships, and processes to help you teams scale? Request a demo and we’ll be happy to show you.

It is widely known that it costs five times as much to acquire new customers than it does to keep existing ones. Renewals are tied to more than just LTV (Lifetime Value), they also directly influence customer acquisition costs (CAC), budgeting, margins, and brand reputation. According to Gartner Group Statistics, 80% of your future profits will come from 20% of your existing customers. Renewals are the lifeblood of SaaS businesses. This is why we are excited to announce our latest language model that detects when customers are conversing about - Renewals.

Here is how it works.

Sturdy is an AI + automations platform that unites all user language and converts it to data so that other people, teams and products can leverage that data. For example, when a user says, “Hey, when is our renewal date?” in an email to the accounting team (or through any other channel), Sturdy will flag it. Next, using Sturdy's no-code automation engine, the signal is routed to the appropriate account manager or teammate (who likely would never have been notified or notified too late) who can take action.

In addition to relying on top-of-the-funnel demand gen activities to bring in new business, leaders must turn their attention to a key source of growth hiding in plain sight- your customers (we have a signal for this too - Expansion). Systematically detecting critical business signals like renewals and ensuring that the appropriate people on your team are responding quickly needs to be part of your growth playbook.

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Whether your SaaS business serves 500 or 500,000 users, success hinges on relationships with your users. Unfortunately, listening to users isn't all that easy given the volume of everyday communications and the number of tools in play like email, ticketing systems, chat, video calls, Gong, etc. To compound matters, users are often communicating with multiple people on multiple teams. Deriving value from this data set that is practically hiding in plain sight has been, until now, nearly impossible.

Enter automation. Automation allows you to better understand your relationships with users and provide a better user experience in the process. Automation in this context is the process of using AI and robotic process automation to discover insights and trigger actions at scale.

Introducing Sturdy Automations

Take your workflows a step further with our automations! This new powerful functionality allows you to create your own automated workflows without writing a single line of code. Build out new combinations tailored to the needs of specific teams that want more information about your company’s users. Then extend workflows in the systems they work in the most. Today, Sturdy customers can begin adding custom automations in a few easy steps. 

Step 1. Choose a Signal

The first step in building your automations is to pick a signal. A signal is transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights. Customers send signals that help us predict churn, capture references, get in front of renewals, prioritize features, and just run our businesses better. Our customers are giving us this information every day in email, tickets, chats, calls, and more. In fact, we know that nearly 17% of all user-to-business communications contain a signal.

Step 2: Select a field

Depending on the signal you've chosen in step one, you will then select a field. A field is a set of identifiers and attributes that describe a customer. Common examples of a field may include the customer ARR, segment, territory, support level, even custom fields are pulled into Sturdy. The list of fields is populated via API from your master customer database of record like Salesforce.com. 

Step 3: Set a value

Now that the first part of our automation (signal + field) is ready, it is time to pick a value. Like fields, values are populated from your master customer database sent to Sturdy via API. For our automation, we selected the field Salesforce Account Customer Segment. The corresponding value in Salesforce is a monetary value imported from a pick list in Salesforce.com.

Step 4: Pick someone to notify

Your automation recipe is nearly complete. Now you just need to pick someone or a system to notify. To customize the exact notification that will occur, pick a notification method. Email and Slack are automatically enabled and available today. In just a few weeks, you can add Salesforce, your CSP, Jira, etc. 

What’s next. 

In the next few months, we’ll empower Sturdy users to create longer and more complex automation recipes with multi-step automations. And, later this year, our plan is to create a library of pre-prepared recipes to make it even easier to get started. 

Let's talk about the potentiality of happy users. They stay with your business longer and, on average, they spend 67% more than new customers. The power of user advocacy is punctuated by the demonstrable success of NPS leaders. In Fred Reichheld's recent book, The Ultimate Question 2.0 he notes that over the past decade the firms with the highest brand loyalty and subsequent NPS scores returned five times the U.S. median (for public companies with +$500m in revenue).

Happy users often require less support and inspire your customer-facing teams to deliver similar experiences across your user base. They provide valuable testimonials, reviews, references, and case studies. That’s we developed Sturdy's - Happy Signal. 

Here’s how it works. We’ve built technology that detects items of importance like user happiness, among other things, in user-to-business communications like email, support tickets, video conferences, chats and more. For example, when a user responds to an email or support ticket with, “I can’t thank you enough --- you just saved me so much time! You’re the best!”,  Sturdy will instantly recognize this as a signal, flag it, and get it to the right teammates.

Most businesses use CRM, spreadsheets, and reference management tools as the go-to location to find and request references but they lack functionality to build a sustainable customer reference pipeline. Continuously building a pipeline of references is a key use case and measurable value proposition for Sturdy.

Sturdy is like a lead generation tool for customer references. On average, businesses using Sturdy see a 2.5x increase in customer references in the first 6 months of getting started.

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The Response Lag signal calls out when customers are waiting for responses from customer-facing teams and are chasing your associates for updates, actions, access, etc.

Sturdys newest business signal is live in customer accounts. The Response Lag signal calls out when users are waiting for responses from customer-facing teams and are chasing your associates for updates, actions, access, etc. How do we do this? We start by ingesting every customer communication (emails, tickets, calls, chats, etc.). Then we use NLP/AI to discover signals like Response Lag. Next we transmit those signals to the people and systems so action can be taken.

While top line monetization opportunities tend to get the attention, often the biggest, near-term lift for B2B SaaS and SaaS-enabled businesses is operational in nature. The Response Lag signal gives managers insights into areas for service improvement and illuminates coaching opportunities that, ultimately, help to foster better relationships with customers. ‍

Next up is our Security signal. It detects when Customers indicate in their conversation some sort of security concern, like: “Have you had a data breach?” Appropriately, look for the red customer signal called Security. 

Expansion is a critical stage of a successful SaaS growth strategy and the overall customer journey. It’s all about further monetizing the customers you have, and broadening your footprint so you have a larger target market to pursue. That’s why we are excited to announce that we’ve added a new customer signal to our AI-powered customer intelligence platform called “Expansion”. 

The new Expansion signal empowers Sturdy users to identify when their users express purchasing intent like adding more users, buying services, or upgrading their plan. 

Given the volume of customer conversations across various communications channels, valuable customer signals like those that imply account growth are often trapped in layers of technology, across multiple teams, gathering digital dust. Our newest signal, Expansion, cuts through the noise and across silos to help customer success and account management teams seize on critical upsell opportunities. 

Want to get Expansion signals? Getting started is refreshingly easy and won't strain your internal resources. 95% of the initial work to get started is done by the team at SturdyAI. Sturdy leverages data that you are already collecting with existing systems (email, CRM, ticketing systems, video conferencing, etc) and can be configured to leverage those same systems to receive insights so your teammates can work in the platforms they are most accustomed to. Clients typically start receiving their first customer signals in less than 4 weeks. Realizing value thereafter is nearly immediate. No change management or IT resources required!

Sturdy's Data Engineering team has been hard at work developing new customer Signals. Late last month, the team added a new customer signal to our AI-powered customer intelligence platform called “How To”. This signal detects when your customers ask, “How do I do this?”. By listening for this type of interaction, SturdyAI users get immediate access to insights like:

• Which new or existing features do your customers need help with, either because they are confused by them or because they are very interested 

• When seemingly small UI/UX issues become trends

• Which customers could benefit from more training, helping you to develop your champions

• How to improve your knowledge base, help text, and self-serve content   

Next up, we’ll launch our new Expansion Signal. This signal will help to identify when your customers express purchasing intent like adding more users, buying services, or upgrading their plan.   

It’s weird to be happy to announce a new customer signal called ... Unhappy. Strange but true. New to Sturdy’s AI-powered customer intelligence platform is the Unhappy signal. The new model detects negative sentiment and customer frustration in emails, support tickets, chats, and video calls. 

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The Unhappy Signal is the first of many new Signals to come.

Unhappy is one signal in a series of new signals that Sturdy’s Data Sciences team is developing. The team is also exploring innovative ways to correlate causes of the negative sentiment with specific signals. For example, Sturdy will be able to show how specific bugs or account leadership changes impact customer sentiment. For those less familiar with what we are developing, the Sturdy platform scans emails, chats, support tickets, and other related customer communications. It then automatically detects signals that impact revenue, product roadmaps, references, and more. 

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Customer success, account management, customer marketing, and product teams use Sturdy.

Customer success, account management, customer marketing, and product teams can now more easily surface what is occurring, but also discern why it is happening because the platform automatically provides contextual highlights. That’s critical because all too often, the onslaught of customer communications is smothered by the sheer volume of messages. These large unstructured data sets stored in multiple systems in the cloud are not easy for companies to use on their own 

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No training, no change management required.

We aren’t here to reinvent and change the way teams or companies work. And that is what is so exciting about what we do. SturdyAI is the force multiplier for your business. If you already have a cutting edge BI tool, we just give it better data. If you have a killer CX app, we make it more insightful. If you have great Customer Success, Account Management, Operations, Marketing, and Product teams, we make them more efficient and provide them with better data.

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More about us.

Led by a team of seasoned founders and B2B SaaS experts, Sturdy.ai is unlocking massive value from data hiding in plain sight. Using AI, Sturdy helps P&L holders preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter's results. Sturdy’s AI-powered customer intelligence platform detects critical signals from your customers and routes them to the right people at your company in real time, unlocking value and reinforcing process execution. 

Sturdy.ai is joining fellow SaaS technology innovators Prodoscore and Update.ai to share our solutions with the Colorado Customer Success community. Members will learn about the newest technologies available to customer success teams in an engaging format featuring live product demonstrations. 

Customer success leaders and team members will  briefly kick the tires on some exciting new offerings that can help lift customer retention rates, deliver better customer experiences, and increase productivity.‍

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When: Wed, October 13, 2021 at 4:00pm MDT

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Where: Register for free here

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Who should join? 

If you're a cloud computing (SaaS, IaaS, PaaS, MSP, mobile) manager whose mission is to onboard, serve, retain, and grow customer relationships, this regional community is for you! Meetups feature networking, learning, and sharing ideas to combat customer churn and increase loyalty. This is a local chapter of the Customer Success Association (http://www.customersuccessassociation.com). Topics include new technologies, "best practices," management systems, and people dynamics. Attendance is free and all are welcome.

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About Sturdy:

Led by a team of seasoned founders and B2B SaaS experts, Sturdy.ai is unlocking massive value from data hiding in plain sight. Using AI, Sturdy helps P&L holders preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter's results. Sturdy’s AI-powered customer intelligence platform detects critical signals from your customers and routes them to the right people at your company in real time, unlocking value and reinforcing process execution. 

Sturdy, a revenue retention solution using AI-powered conversational analysis that identifies opportunities and preempts risks hidden in everyday customer conversations, is pleased to announce its integration on the Zendesk Marketplace.

Sturdy has developed an integration with Zendesk that enables Zendesk customers to tap into data that, for most, has been hiding in plain sight - the customer-generated content of tickets and chats within Zendesk.

Sturdy already works with Zendesk customers and helps them by:

• Increasing customer retention rates by .5-2%: Sturdy surfaces actionable insights that signal indicators of customer churn like executive and sponsor changes, contract requests, poor sentiment, and more. 
• Increasing customer lifetime value by 5-15%: Sturdy amplifies the unbiased voice of the customer while detecting customer signals such as feature requests, bug reports, outages, renewals, and upsell opportunities. Use of these signals enables teams to better understand their customers’ needs. 
• Increasing team member efficiency: Sturdy’s customer signals cut through the noise of email and tickets so team members can resolve the most revenue-sensitive issues quickly and with the relevant context. 
• Increasing customer references by 10-25%: Sturdy listens for signals of referenceability and serves a lead-generation for customer marketing and customer advocacy teams. 

The integration between Sturdy and Zendesk involves the use of Sturdy's AI technology to detect critical custom-generated signals from everyday communications like tickets and chat sessions. Once detected, customer signals are routed to the appropriate team members to take action resulting in revenue preservation, revenue generation and the gathering of critical trends that provide insights into customer behaviors. 
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"We are excited to partner with Zendesk and we share their mission to improve customer experiences," said Joel Passen, one of Sturdy’s co-founders. Leveraging AI and ML, we turn previously underutilized sources of customer content (tickets and chats) into actionable data that amplifies the voice of the customer and automates critical processes resulting in improved customer outcomes and, ultimately, revenue retention for SaaS enterprises.”
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To learn more about Sturdy's products, please visit sturdy.ai
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To learn more about Sturdy's integration with Zendesk, go to https://www.zendesk.com/apps/support/sturdyai/?q=mkp_sturdy
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About Sturdy:

Led by a team of seasoned founders, Sturdy is unlocking massive value from data hiding in plain sight. Using AI, Sturdy helps P&L holders preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter's results. Sturdy’s AI-powered customer operations platform detects critical signals from your customers and routes them to the right people at your company in real time, unlocking value and reinforcing process execution. 

It happens all the time, and you’re often the last to know. Your sponsor, once your economic buyer and advocate, is on the move. Gone. Losing an executive sponsor or senior point of contact is a catalyst for churn. Often “Executive Change” is reported as unavoidable churn. But is it? ‍

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Here’s How It Happens

Surviving an executive change is possible - even likely

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Surviving an executive change is more realistic if you have a plan. Winging it and leaving a save to chance is not a winning solution. Your plan needs to start well before you receive news that your sponsor has departed. Ideally,  you need to start by understanding your customers’ organizational structure and power chain. You need to understand how decisions get made. Post-sale teams should continually blueprint accounts looking for additional executive-level advocates. Also, risk is mitigated when you leverage your champion to create co-champions that will advocate for you when there is a shake up. A good rule is to create and foster at least three key advocates within each customer account. Ideally, these stakeholders should be cross-functional representing finance, IT, and functional teams. 

Even when you do have a process in place to address loss of sponsor, the news is often blindsiding. More likely than not, executives don’t share their transition plans with anyone outside their org with advance warning. Otherwise, signals of change are often unconsciously ignored due to the sheer volume of communications your team is dealing with. Worse yet, what if requests like our example above land with a teammate that simply responds with a copy of the contract unaware of the gravity of the situation? 

If your heart is racing and your palms are sweaty, you’re not alone. We’ve been there. That is why one of the first language models that we developed and trained when we started Sturdy was executive change. 

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Detecting customer Signals ‍

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So how do you detect executive change signals? There are some hacks out there. The easiest to implement is one that leverages LinkedIn Sales Navigator. If you have a paid account, set up “Career Change” alerts in LISN. This will work for smaller companies with 20-50 customers but gets too noisy at any kind of scale. The big constraint is that you can't filter the alert by decision makers only. This would be a good feature for LISN though by the time your DM updates their profile with a new role, the window of opportunity to save the account likely will have closed. 

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At Sturdy, we use our own product to detect executive change signals. Sturdy analyzes emails, tickets, chats, and video calls listening for signals of executive change. When it detects language synonymous with the loss of a sponsor, it flags the conversations and alerts our stakeholders immediately. Our alerts are sent to a Slack channel called #executive-change. At our stage, this is quite effective and still manageable. Eventually, we’ll connect Sturdy to our case management tool creating a more sophisticated closed-loop process.  

Below is the same message from the top of this post but this one was run through the Sturdy AI Inference Engine. It’s been accurately flagged with customer signals indicating executive change and a high probability of churn. This message triggered a real time alert to our customer operations team. 

Reacting to an executive change

We think about signals as lead generation for inquiry and action. And, as with sales leads, acting with urgency yields the best outcomes. Borrowing from our sales / marketing SLA, our requirement is to follow up on executive change signals inside of 1 hour. This makes us seven times more likely to schedule a meeting with the customer in the same week as the signal was received. Having a set timeline, we prevent procrastination and promote action.  

Otherwise, we have a defined play that we run. The play has 3 phases and we train our workmates on this and other plans on an on-going basis. Here is an outline from our post-sales playbook for executive change. 

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The loss of an executive sponsor is a red-level risk event. Winging it doesn’t save customers. You need a defined process in place to mitigate account churn and solution downgrades. Team members need to investigate the account vitals quickly. Information should be gathered from other client stakeholders. If a new sponsor is in place, a briefing should be scheduled ASAP. Show the new leader what’s in it for them. Clearly emphasize the value your solution delivers. Minimize their risk. Show them the future. Give them an easy win. 

A reminder of why it matters 

The B2B SaaS industry is maturing quickly. Competition is fierce. Category leading post-sale teams focused on customer retention and monetization are building capabilities to significantly contribute to top line growth. For example, A $100M ARR Company with 2000 customers saves 30 customers in Year 1, dropping its churn from 8 to 6.5%. By maintaining this churn rate, its revenue in year 1 will be $1.6m higher. By year 5, $25m, and by year 10 almost $170m higher (50k ACV, 5% upgrade rate, 30% growth rate). Look at these numbers through an investor’s lens where some companies are valued at 25x earnings. Those are some real numbers. Saving a couple dozen customers a year really adds up. 

Summary

The loss of an executive sponsor is a red-level risk event but it doesn’t need to be fatal. 

1. Preventative measures like fostering multiple executive-level relationships to develop cross functional advocates significantly mitigates risks. Go wider. Go cross-functional. Have no less than three key executive contacts at every account. 
2. Building a process or deploying technology to detect risk is key. Knowing is more than half the battle in this instance. 
3. Creating a defined process to manage a loss of sponsor event is imperative as is training team members to respond with urgency. 
4. Creating a culture that reinforces the importance of retention and customer monetization is a key to motivating high performance post-sales teams. 
   

20% of all customer content contains a critical signal. For B2B SaaS businesses, these signals are immensely important. They often indicate if our customers are willing to grow with us or if they are growing away from us.

Customer Signal
(noun) a gesture, action, or transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights. 

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Customers send Signals that help us predict churn, capture references, get in front of renewals, prioritize features, and just run our businesses better. Our customers are giving us this information in Slack, Email, Salesforce, Webinars, training sessions, quarterly business reviews, Zoom calls, etc. 

For B2B SaaS businesses, these Signals are immensely valuable. For example, reducing churn from 10% to 9% in a $10 million ARR business means that every customer is worth $17k more in lifetime value (500 customers, $20k annual contract value). And reducing churn in this example is saving just five customers a year. 

Examples of customer Signals

Identifying, classifying, and escalating customer Signals to the right people at the right time empowers companies with information and insights to preempt issues before they spiral and seize revenue opportunities to improve the bottom line. 

For example, when a customer asks, “Can I have a copy of our contract?” in a support ticket, a Signal is sent. In a SaaS environment, the customer is likely signaling risk. Maybe they are evaluating a competitor. Maybe there has been an executive change or a shift in priorities. Regardless, every SaaS leader will agree that this signal needs to be escalated so action can be taken. 

Below are a few other examples of customer Signals. This is not an exhaustive list; every company will vary on what is important. An interesting exercise is to sit down and list out the Signals that your teams should be watching for. The output of this exercise can be used to improve operations, user experience, training workflows, and more.  

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Where to find Customer Signals

Most of us have given our customers the ability to communicate with us using a variety of channels. After all, we want to hear from them. This allows us to gauge their health, status, and likelihood of buying more of our products and services. 

Given the prevalence of multi-channel communications workflows, critical Signals are often trapped in layers of technology across multiple teams, gathering digital dust. The most common scenario for most businesses is that important customer Signals are hiding in plain sight. They’re trapped in email accounts, ticketing systems, call transcriptions, chat logs, and CRMs. And for most of us, the only way we utilize this information is if someone manually identifies, records, and escalates it.

How to use customer Signals 

In today’s competitive SaaS environment, the most successful companies are learning to “listen” and interpret the Signals that their customers are giving them about their products and services. The category-leading companies are doing this at scale - automatically. 

With SturdyAI, teams can easily sign up for alerts on specific Signals, accounts, and even competitor mentions. For example, the most appropriate team member in any group can get an alert whenever:

• One of your customers requests a copy of their contract or asks about their renewal date
• An account has a new executive, point of contact, or executive sponsor
• A user asks for information about adding more users or adding a new product or service
• One of your customers mentions one or more of your competitors
• A user reports an outage or bug
• A customer is signaling satisfaction and, ultimately, referenceability
  

What’s exciting about customer Signals

Customer Signals undoubtedly help us understand our customers better. Specifically, by defining and leveraging Signals at scale, we can have a clear understanding if our products are delivering the value promised at the time of the sale. We can also better understand if our customers are willing to grow with us or if they are growing away from us. 

Rapid advancements in technology, especially AI, are making it easier to help brands quickly and responsibly use data to understand customer behaviors and predict customer needs. When we have the ability to discover new patterns and insights in our data, we are better able to anticipate future decisions. In the end, harnessing customer Signals presents opportunities—and incentives—to deliver better service and find new ways to grow.

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Our customers are telling us what they want and need every day - with nearly every message. Customers give us information to run our businesses better, predict churn, capture references, get in front of renewals, prioritize features, and more. Until now this data has been trapped and left to decay in dozens, if not hundreds of data silos.

This week SturdyAI is in the spotlight at CS Insider, a community comprised of all things customer success, curated for busy professionals. In this short video, we walk through how SturdyAI is helping businesses detect signals that empower teams to preempt risks and discover opportunities that impact the bottom line. 

Highlights

• Analyze customer communication channels like tickets, email, chat, and voice for valuable insights.
• Accelerate data into action so stakeholders can quickly act on what’s most urgent.
• Tap into and leverage a valuable new data set that you’re likely ignoring today.

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Interested in finding what's hiding in your customer communication data? Get in touch with us.

Slack has become the de facto tool for internal communications for many teams. By shifting internal communications out of inboxes and into channels, teams can work more collaboratively while reacting to critical issues in real-time. This is why we are excited to announce that SturdyAI is now integrated with Slack. Now teams that rely on Slack to collaborate can create custom channels to receive important signals from SturdyAI.

For those less familiar with SturdyAI, we’ve built a product that analyzes business language(support tickets, emails, customer chat sessions, video and phone call transcriptions, etc.) for important signals that impact the bottom line. For example, when a user asks, “Can I have a copy of our contract?” in a support ticket, our product instantly recognizes this as a potential cancellation signal, flags it, and alerts the team in charge of triaging accounts. Today, our AI today recognizes 7 distinct signals, with dozens more currently in development. And the cherry on top is that SturdyAI gets smarter with every message.

Here’s how it works. 

Step 1 

First, a customer communicates with their vendor via email, support ticket, chat or recorded call. Below is an example of a ticket submitted through a ticketing system. 

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Step 2

Next, SturdyAI ingests and analyzes the ticket in real-time looking for important signals. In this ticket, there is a critical issue. The customer, Brightlight, is asking for a copy of their contract. This is a clear signal that this customer may be at risk. Furthermore, the customer is indicating that they have purchased a product that may have similar features reinforcing the urgency of this message. Below is the original ticket that SturdyAI analyzed and applied the Strong Churn signal.

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Step 3

Now that SturdyAI is integrated with Slack, critical signals are  “chirped” into Slack channels.  SturdyAI’s customers create their own Slack channels to receive critical signals. Integrating SturdyAI takes minutes. When SturdyAI is integrated with Slack, critical signals are  “chirped” into Slack channels.  SturdyAI’s customers create their own Slack channels to receive critical signals. We've seen some great use cases already. Here are a few of our favorites.

#competitor-mentions

#customer-references

#executive-change

#feature-request

In this example, we’ve created a “Churn Alerts” channel. These customized channels provide teams and leaders with real time visibility into critical customer issues so the right people can take action before it’s too late. Below is a screenshot of our churn-alerts channel and the alert that was triggered by the original message that this customer sent about requesting a copy of their contract.

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Why now?

‍Integrating with Slack was moved to the top of our feature roadmap as the pandemic has created new challenges for our customers related to remote operations. Less in person attendance by customer-facing teams means widening internal communication gaps at each stage of the customer lifecycle. Plus, integrating with Slack just isn't that hard. In the coming weeks, we will continue to refine the integration and, ultimately, the user experience making it easier for users of Slack to get mission-critical signals from customers in the apps that they use most.

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Customers willing to serve as references for your solution often make the difference between opportunities that result in closed/won or closed/lost. However, harvesting these references can be challenging, time-consuming, and resource-intensive. While critical to the bottom line, the responsibility for gathering new references often falls on marketing and product management teams who may not be as close to the individual users as their counterparts in sales, support, and customer success. This compounds the complexity of gathering new references.

While customer reference software platforms are often good at providing a go-to location to request references and to find approved reference content, they lack functionality to build a sustainable customer reference pipeline. Continuously building a pipeline of references is a key use case and value proposition for SturdyAI.

Using AI and machine learning while leveraging your existing tech stack, SturdyAI empowers customer reference teams to automatically capture authentic referenceability signals from everyday communications with your customers. It's automated lead generation for your customer reference program.

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It’s impossible to overstate the value of customer references. Whether you have $1m or $100m in ARR, when your customers demonstrate how they’re using your solutions, future customers see themselves in these examples bringing to life the value of your offerings. A strong reference from a current customer is so powerful that it can even transcend fierce competition, the norm for most of us in the rapidly maturing B2B SaaS industry.  

Unfortunately, the reality is that even the most enamored customers aren't likely letting anyone else know about you. In studies from two industries, only 10% of the “promoters” in NPS surveys actually referred profitable business. Plus, executing a customer reference program takes lots of discipline and resources. For these reasons, we decided that this is a problem worth solving.  

It has taken us over a year but we’ve cracked the code and productized a scalable way to harvest more customer references. Fortunately, we didn’t need to look very hard to find the signals that lead us to believe a customer is referenceable. The answers were right under our noses all along in the day to day interactions that our teams have with customers. That’s right, customers are signaling their willingness to provide references, testimonials, positive reviews and the like every day.

Here’s how it works. We’ve built technology that detects items of importance like customer references (among other things) in customer-to-business communications (email, support tickets, video conferences, etc.). For example, when a customer responds to an email or support ticket with, “I can’t thank you enough --- you just saved me so much time! You’re the best!”, Sturdy will instantly recognize this as a potential reference signal, flag it, and alert the appropriate person or team. We’ve even set up integrations with Slack and Teams so when a potential customer reference is detected, Sturdy chirps the notification right into a #customer-reference channel. Say hello to a self-sustaining customer reference funnel.

Getting started is easy.

Set up takes less than an hour for most operations / IT teams. We installed the Sturdy Salesforce.com app from the AppExchange (this is in private beta at the moment and we are accepting new users weekly here). Next, we synced our customer success and support teams’ email accounts (we use Gmail but we have an Outlook integration as well) with Sturdy’s email ingestion API. Once connected to the data sources where customers communicate with us, the rest is really easy. Time to value is a matter of days and weeks. And, there is no significant change management required. Turn it on and let the machine run. The cherry on top is that the AI gets smarter with every customer message. 
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1.  Log into Sturdy (if you have a Slack or Teams integration you can skip step 1) and select the “Reference” signal. Sturdy will immediately surface any customer communications that contain potential references. Screenshot below. 

Below is an example of a customer reference signal that I found this morning. Note that I have a privacy feature enabled here that anonymizes the data for the purposes of privacy and compliance.. In this message, Henry Goldberg is effusive in his praise for the product and the level of service provided to him. 

2. Next, Sturdy alerts our customer marketing team of a new potential reference. Upon receiving this signal, our team will gather some information about the account and the user and determine what type of reference we want to ask for (peer-to-peer, review, case study, referrals, testimonial, etc) and who will make the request.  Here’s another example of a customer all but volunteering to be a reference. Based on the anonymized aggregate data of our B2B SaaS customers, we've found that >1.5% of all customer communications include a customer reference signal.

3. The final step is to reach out to the customer with your “ask”. Every team is going to have some nuance here. We use a couple of different “plays”. Our favorite is the progressive / multi-step “swag+” play. When our team receives a reference signal, our CS and support teammates are empowered to ask our customers for an address where we can send a care package of swag. A few days later, we let the customer know that their Sturdy swag is on the way and then we ask if the customer would consider serving as a reference. Our success rate when using this play is nearly 100%. 

Creating a self-sustaining customer reference funnel starts with consistently detecting the right signals and getting your customers’ voice to the right people at the right time. These signals are pure gold to every customer marketing team. The best part is that unlike traditional, resource -intensive customer reference strategies, Sturdy makes it virtually automatic. Turn it on and let it rip. Get a steady stream of potential customer references delivered in Slack, email or via dashboard - however you choose.
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Want to get more customer references?

If this sounds interesting, our enterprise beta program is in full swing. If you are interested in creating an automated customer reference funnel, here is a link to register for our public beta program. 

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The SaaS industry is still roaring towards ubiquity. Blissfully’s 2020 SaaS Trend Report notes that overall spend per organization on SaaS-based products is up 50%. However, the report also notes that this is down from previous years, and the growth rate seems to be slowing. This gradual slide has the industry turning its attention to optimizing for customer retention and leveraging existing customers for substantive growth.

Anymore, churn is just SaaS slang. Churn as a metric is confusing and ambiguous. There are too many ways to calculate customer and revenue churn. Analysts and investors have been increasingly skeptical of churn rate calculations for years. Anymore they just want a raw data dump from companies so they can run their own math.   

“There are too many darn ways to calculate churn. That makes it ambiguous.” - Dave Kellogg

The focus is on net dollar retention (NDR)? 

NDR has emerged as one of the top SaaS metrics that matter. NDR takes into account upgrades, downgrades, and churn to quantify how much recurring revenue from current customers you retained across a defined period of time. There are two hugely important questions that NDR can answer.

1. Is your product delivering the value promised during the sale? 
2. Are your customers growing with you or without you? 
   

What makes net retention so powerful is that for most companies, it’s cheaper to sell to existing customers than to sell to new logos. This makes net retention the most cost efficient way to accelerate revenue growth.

Calculating net dollar retention.

If your NDR is over 100%, this means that an increase in revenue is attributable to your existing customers. Here’s how to calculate NDR. 

(Starting MRR + expansion — downgrades — churn) / Starting MRR  = NDR

Let’s say you start the month at $100,000 in recurring revenue (MRR). Over the month it added $25,000 in expansion revenue, has $10,000 in downgrades and another $5000 in churn. ($100,000 + $25,000 — $10,000 — $5000)/$100,000 = 110% NDR. Your MRR is $110,000 with an NDR of 110% This is good. Essentially, your upgrades / upsells lifted your revenue despite losses. ‍

What good looks like.

At least 100% is considered a good NDR rate for SaaS businesses selling to the SMB market. Selling to smaller accounts naturally yields a lower NDR. SMB clients are less financially stable, ripe for acquisition, and have smaller budgets.  A good enterprise NDR is 130%. As with many SaaS metrics there are other things to consider. For example, Workday’s NDR is 100% but gross retention is 95%. Either Workday is very good at selling the “whole” deal or their product footprint presents limitations. 

Here are some examples of net dollar retention rates for some interesting SaaS and SaaS-enabled companies.  

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Caring about net dollar retention.

NDR provides a revenue-based view of customer retention. NDR is increasingly important as you scale from a small to medium-size business and beyond. For example, a $5m business that churns 20% can replace that $1m with net new business when it’s growing +50% a year. But when a $30m business needs to replace $6m this becomes insurmountable especially if the growth rate is slowing.

The effects of NDR compound with time. It’s either additive or punitive with every customer that you acquire. This means that small upticks in NDR can add up to very large differences in total revenue over multiple years. For example, assume a business had $10 in revenue last year and consistently generates 20% of revenue from new customers. Improving the NDR from 95% to 105% may sound meager, but over five years the business will gain another $5m in revenue. 

Lifting NDR and a plug for Sturdy as a solution to help.

How can you start identifying more opportunities to grow and deliver value? Here are two ideas that sound great in articles and when delivered by panelists at conferences. First, hire a great team of CSMs who are well enabled and know your customers intimately. Second, develop more premium services to sell your customer base. Frankly, these are right answers but they take a lot of time, resources and change management to create an enduring impact. 

Now consider this. What if you had a “tool” that could analyze customer emails, tickets and conversations for important signals that are typically related to predicting churn? Maybe something that can listen for suggestions about features and products that might accelerate value capture and lift revenue? What if you could get started with such initiatives without major upfront investments in data infrastructure or change the way your teams work? We might know of such a tool. Hit us up. We’d be just as happy to talk about NDR and our experiences over the years tracking this SaaS metric juggernaut.

The idea for Sturdy was born from asking this all too common question far too many times, “What is going on with Customer X?” And many times over the years we have griped, “How is it the 21st Century and we need to get 5 different people in a room to login to 5 different apps in order to know whether a customer is happy or not?”

This is why every SaaS company has a “Top Customer List”. At Newton, our previous company that was acquired by Paycor in late 2015, we had a rule, “Whenever someone on this list contacts us for any reason, let So-and-So know.” If you think about it, such lists admit a fundamental failure of running a modern business...you only have the time and resources to listen to your most valuable customers (which means you most often ignore the rest).

This was our first slide...

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Our earliest decks talked about, “getting your data in one spot”. But that wasn’t the problem we were trying to solve (wanting to see all the data in one spot is a symptom, not a solution). The problem wasn’t really a communication problem, it was a mining and refining problem. When a customer requests a copy of her contract, that message must get forwarded to the Saves Team - immediately.

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Our “Aha” moment was when we realized that our customers are telling us what they want and need everyday. They give us information to run our businesses better, to predict churn, to capture references, to get in front of renewals, to prioritize features, yet this data is trapped and decaying in dozens, if not hundreds of data silos.

A big problem is that our customers are giving us this information in Slack, Email, Salesforce, Webinars, Training Sessions, Zoom calls, etc.. And the only way we utilize this information is if someone manually identifies, records and escalates it.

Remember when we said it was the 21st century? We still manually identify, capture and route feature requests. And bug reports. And cancellation requests. And sometimes this means that we don’t always see the signal, or we forget to log it, or when we route it, no one pays attention.

But these signals are immensely valuable. For example, reducing churn from 10% to 9% in a $10 million ARR business means that every customer is worth $17k more in lifetime value (500 customers, $20k annual contract value). And reducing churn in this example is just saving 5 customers. 

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Obviously we should do everything possible to mine our customer communications, and yet many companies know more about their anonymous website visitors than their own paying customers.  Almost every company has a way to track and monitor its website visitors, and almost zero have any way to monitor and monetize the happiness of their actual customers.

Here’s a challenge...Answer this: If your company was about to lose a customer, who would be the best person to save that customer? What metrics would you use to support your answer? Most companies have no data to answer this question.

Or, how many times did a customer say, “You guys are great!” last month? How many times were those happy customers converted to references? And how many of those references are delivered to your sales team to help them close new business?

Again, it's the 21st century. Yet we have no analytic capacity or automation as it relates to customer feedback or happiness. But don’t despair. You're not alone.

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We realize the challenges are great. But in this area, failure is truly unacceptable. To have a truly operationalized customer focused company, you need to mine these communications, without bias and without manual data entry. You need something that never gets tired, that doesn’t need training, and that gets better the more you grow and the more you throw at it. And most importantly, you can’t wait until the quarterly business review is complete to triage a churning customer.
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And that’s why we started an AI company. But not just any AI company and not just for the sake of using AI.

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We aren’t here to reinvent and change the way teams or companies work. And that is what is so exciting about what we do. Sturdy is the force multiplier for your business. If you already have a cutting edge BI tool, we just give it better data. If you have a killer CX app, we make it more insightful. If you have a great Customer Success, Account Management, Operations, Marketing, and Product teams, we make them more efficient and provide them with better data.

Sturdy has developed a BI product that analyzes customer communications, detects important signals, and empowers teams with real-time data to act on situations with speed and intelligence.  

We’re thrilled to announce the launch of Sturdy, a ground-breaking business intelligence platform that leverages advanced data science in order to detect items of importance in customer-to-business communications. 

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In simple terms, Sturdy helps people at B2B SaaS businesses leverage a data set that is hiding in plain sight  - data that your customers want you to use.

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Trapped in communication layers, and across teams, are critical signals like, point of contact changes, potential references, churn likelihood, and competitor mentions. These signals gather digital dust in email accounts, ticketing systems, transcriptions, chat software, and CRMs - until Sturdy. 

Customer-to-business communication data is an untapped data frontier. Massive value is realized when the data is aggregated, analyzed, refined, and redeployed. Sturdy was created to empower teams to act on mission-critical situations with speed and intelligence.

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If you wanted your team to capture 10 new referenceable customers, what would need to happen? Or, how many of your customers got a new Point of Contact last month?  Which customers asked for their Renewal Data this week?

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As a leader you want to manage risks and capitalize on opportunities (we call them “signals”).  Signals are sitting in email accounts, videoconferencing transcripts, chat logs, and buried in ticketing systems.  They are manually captured, if at all, and then data-entered into spreadsheets and other systems.  And you have to create, enforce and constantly train people on rules that change the way your teams work.  

Not to mention, there is no analytical capacity.

The idea for Sturdy came from building, bootstrapping, and scaling successful SaaS businesses. We founded SturdyAI to empower businesses to solve problems that we faced as entrepreneurs and executives. Before SturdyAI, the capture of these signals has been inconsistent, fragile and inefficient.

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We’re experienced executives and engineers. We believe that every business has revenue and earnings potential trapped inside of its communications systems.

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In mid-2020, Sturdy received an investment from Super{set}, a team that has created $1.2b in exits. This accelerated our product development and commercial efforts. Partnering with Super{set} was natural. We share the belief that “data is the new oil” and that refining data defines the new basis of competition across sectors and problem spaces.

Many of us worked together at Newton Software. This is a company that we bootstrapped, scaled and sold to Paycor, one of the largest independent HCM companies in the world.  At Newton, we lived by some simple rules. We live by these rules at Sturdy. 

1. If you make a mistake, tell someone right away. We’ll fix it. 
2. We design technology that we want to use. 
3. We sell software how we’d want to buy it. 
4. We support our software the way we would want to be supported. 
5. We do things the right way, not the easy way. 
6. We don’t take shortcuts. 

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We’re energized and ready to roll. Let’s talk. 

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We’re encouraged by the feedback and results from early customers using Sturdy. And, we’re fired up to help businesses preempt customer issues before they spiral and seize revenue opportunities in time to improve this quarter’s results. 

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What will you find in your data? 

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Click here to get access and see for yourself.

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