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The four types of SaaS churn and how to calculate them
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.
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.
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%.
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)
Revenue Churn
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.
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.
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
Gross Churn Rate
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.
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%
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%
Net Churn Rate
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.
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.
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%
Leaky Bucket Equation
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”.
Kellogg defines the leaky bucket equation as “Starting ARR + new ARR - churn ARR = ending ARR”.
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:
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
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.
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.
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.
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%.
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)
Revenue Churn
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.
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.
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
Gross Churn Rate
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.
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%
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%
Net Churn Rate
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.
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.
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%
Leaky Bucket Equation
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”.
Kellogg defines the leaky bucket equation as “Starting ARR + new ARR - churn ARR = ending ARR”.
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:
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
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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Introducing the Confusion and Billing Issue Signals
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.
The top 13 customer intelligence platforms in 2023
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.
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.
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.
Customer and Product Intelligence
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.
Sales-Focused Solutions
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Stop doing these 3 things now to improve your customer retention strategy
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.
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.
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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.
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.
Stop ignoring customer feedback
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.
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.
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.
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.
Stop overpromising
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.
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.
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.
Here are some overpromise signals that were detected in customer-business emails. Sound familiar?
"This is something that was promised in the implementation stage."
"… even excited about the features that were promised. But do feel ... underdelivered on the capabilities."
"Below is a list of things that were promised and hasn’t happened:"
"That was promised, but I still have not received anything."
"We can't use these services that were promised/promoted."
Stop doing Silly QBRs
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.
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
- Make them a conversation, not a presentation.
- Come with more questions than statements.
- 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.
- Make them 50% retrospective and 50% prospective. 100% strategic still.
- 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.
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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Sturdy launches the “Overpromised” Signal
If you’ve ever heard the following - keep reading
"This is something that was promised in the implementation stage."
"We were excited about the features that were promised, but you’ve under-delivered on the capabilities."
"Below is a list of things that were promised and hasn’t happened:"
"That was promised, but I still have not seen or heard anything."
"We can't use these services that were promised/promoted."
TLDR
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.
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.
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.
Intrigued? It works. See Sturdy in action.
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Customer email intelligence
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.
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.
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.
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.
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.
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.”
In building Sturdy, we learned that a customer email intelligence platform must do four things very well, all at once:
- Safely and securely extract only customer emails while ignoring all other emails;
- Accurately merge all of a customer’s information into one view, a “single pane of glass”;
- Classify, categorize and Identify critical themes, topics, and sentiments in each email;
- Route and alert the teams and teammates who need to know.
Safely and securely extract only customer emails while ignoring all other emails
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.
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.
Bottom Line: Extracting customer emails needs to be rock-solid, secure, and highly configurable.
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.
In fact, Sturdy estimates that in a typical B2B SaaS company, an Account Manager spends almost 30 hours per month in Account Review meetings.
Bottom Line: Moving customer email out of the inbox will vastly improve account management and add time to everyone’s day.
Classify, categorize and Identify critical themes, topics, and sentiments in each email
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.
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.
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.
Bottom Line: Modern AI technologies will illuminate insights, topics, and themes in your customer base at scale.
Route and alert the teams and teammates who need to know
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.
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.
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.
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.”
Bottom Line: Customer Email Intelligence will ensure that the correct information gets to the right team every time.
Customer email intelligence. The time is now.
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.
4 stars and frustrated | time to move beyond surveys and sentiment
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.
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.
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.
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.
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.
Surveys are still the status quo
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.
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.
While many of us can’t rid ourselves entirely of surveys, they continue to fall short for these reasons.
- Surveys are a backward-looking tool in an era where customers expect near real-time remedies.
- Survey results are often ambiguous, failing to reveal the cause of customer frustration.
- Survey data is often seen as unreliable and not contextually substantive enough to drive real business impact.
- Surveys are often answered by users with exceptionally positive or negative experiences.
- Survey responses are limited to structured questions, so respondents cannot provide feedback about topics that are not covered.
- Surveys require significant customer time and effort and can be considered annoying.
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.
Sentiment alone is… OK
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.
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.
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.
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.
Tapping a new source of customer feedback
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.
Customer Signal
(noun) A gesture, action, or transmission delivered intentionally or unintentionally by a customer that conveys information, instructions, or insights.
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.
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 in time 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 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.
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.
“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
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.
Customer experience trends: 5 to explore in 2023 | Sturdy.ai
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.
Dirty data continues to be a drag.
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.
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.
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.
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?
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.
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.
Here are the top trends I am watching for in 2023.
Dirty customer data gets its day.
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.
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.
Customer data democratization grows.
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.
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.
Data-driven insights overshadow surveys.
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.
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.
The activation of AI-fueled automation.
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.
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.”
Get ready to leave dirty data in the dust.
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.
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.
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.
“What are we building next?”
Since starting Sturdy, we have learned that about 50% of support tickets and 15% of emails contain a roadmap-informing data point.
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.
Answering “what to build next?” isn’t always easy, but explaining “why we are building this next?” never is.
(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.
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.
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.
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…
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!”
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.
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.
Don’t hesitate to contact me at steve@sturdy.ai if you have any questions or comments.
Valuize & Sturdy: Uncover data in your blind spots to maximize customer insights
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
Interview Annotations
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
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.
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.
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?
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?
0:03:24.2 S2: Yeah. It's the same thing. Mine aren't that furry, but it's kind of same jam.
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?
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.
0:05:16.3 S2: I would hire that person really early in my life cycle.
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.
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.
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.
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.
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.
0:08:10.4 S2: Yeah. Yeah. They've got one or, and they're kind of like trying to find food.
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.
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?
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.
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?
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.
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.
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?
0:14:13.8 S1: Like where is it derived from today? Yeah.
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?
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.
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.
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.
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.
0:16:38.2 S1: Can I have a copy of our contract?
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.
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.
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.
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?
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.
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.
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?
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?
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.
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?
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.
0:26:10.9 S2: Yeah.
0:26:11.4 S1: Yeah.
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.
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.
0:27:11.4 S2: No offense to Topeka constituents, but yeah. Yeah.
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.
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.
Six strategies to combat customer attrition
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.
What is customer attrition?
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.
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.
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.
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.
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.
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.
Six strategies to combat customer attrition
1. Listen to customer feedback and act on it.
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.
2. Make sure customers are getting value.
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.
3. Keep communication open and transparent.
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."
4. Maintain product-market fit.
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.
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.
5. Offer customer incentives and loyalty programs.
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.
6. Provide a reliable and proactive customer experience
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.
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.
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.
Conclusion
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.
