AI & ML

Customer feedback: Use AI and listen to your customers, or somebody else will

By
Joel Passen
March 15, 2023
5 min read

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

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

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

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

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 Relying on outdated survey methodologies like NPS and CSAT can be tempting.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

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


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

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

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

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

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

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Customer Retention

Improving Revenue Retention in 2025

Joel Passen
November 15, 2024
5 min read

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

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

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

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

Customer Retention

Burton's Broken Zippers

Steve Hazelton
November 15, 2024
5 min read

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

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

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

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

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

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

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

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

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

AI & ML

Navigating AI Ethics

Joel Passen
October 14, 2024
5 min read

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

What Is Ethical AI?

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

Sturdy’s Commitment to Ethical AI

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

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

Human Oversight and the Role of AI

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

Final Thoughts

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

How many customers will you have to lose before you try Sturdy?

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