Mike Lyons shows how AI reveals what customers actually do versus what they claim. At Technova Taskflow, 3,000 users requested features they barely used, while pattern recognition and sentiment analysis uncovered three real user types: power organizers, simple trackers, and collaborative communicators. The gap between stated needs and behavior is where churn happens.
You know that feeling when you think you understand your customers, but then they do something completely unexpected? Like when Netflix users kept watching the Office instead of trying the new original series that everyone was talking about? Or when customers say they want more features, but your usage data shows they only use three basic functions. The gap between what customers say and what they actually do is where most businesses tend to get stuck. But here's where AI becomes incredibly useful. It can spot patterns in customer behavior that might be invisible to the human eye. Let's think about how this would work using our Technova case study. Remember, Technova's taskflow has about 3,000 active users, but they're losing customers and getting endless feature requests. And given that the product team, well, they think they need to build more features. But what if the real problem is something else entirely? This is where AI techniques for uncovering customer behaviors come in. You might even think of AI as a detective that never gets tired of looking through massive amounts of evidence. So let me give you three techniques to uncover customer behaviors using AI. Technique one, pattern recognition. This first technique focuses on analyzing user behavior data to find patterns. AI can process thousands of user sessions and spot trends that would take humans months to identify. Now, for Taskflow, we might feed AI our user activity logs. Every click, every feature used every time someone logs in or out. And the AI might discover that users who create more than five projects in their first week, well, they're more likely to stick around long term. Or that teams that use the comment feature within the first three days have a much higher engagement rate. But let's go another layer deeper here and really tap into AI's potential. See, AI, it doesn't just tell you what happened. It can identify the sequence of behaviors that lead to success or failure. Let's consider that maybe users who struggle, they're not actually using Taskflow wrong. Maybe they're just missing a crucial onboarding step that successful users naturally figure out. Or maybe they're skipping the team setup phase and trying to manage projects solo, missing the collaborative features that make Taskflow powerful. The point is, AI can show you that struggling users, well, they behave differently. Your AI can show you these behavioral sequences, the steps and the patterns, and how one path leads to success, while another leads to frustration. Now here again, just a couple reminders. Be mindful of data privacy. Be sure to not upload or share any sensitive information that you wouldn't want made public. And also, again, this is where your understanding of your customers and your product becomes essential. It takes human insight to understand why that Behavior exists, and of course, what you should do about it. We always want a human in the loop. Okay, on to technique number two. Sentiment analysis. The second technique involves analyzing what customers actually mean in their support tickets, their emails, and their chat messages. Think of it as sort of reading between the lines. Well, read between the lines. AI can read through thousands of customer communications and identify emotional patterns and recurring themes. Back to Technova. This might reveal that when customers say, I need more features, they're actually expressing frustration about something specific. Maybe what they're really asking for is the existing features to work better in their workflow. Or it might reveal that when customers say the mobile app needs work, they're not talking about bugs or missing features. They're frustrated that they can't access the same project visibility on mobile that they have on desktop. This technique helps you understand the emotion behind customer feedback, not just the words they use. And that brings us to technique number three. Behavioral segmentation. This third technique, it's using AI to group customers based on how they actually behave, not just sort of demographic information. See, traditional segmentation might group customers by company size or industry, and that's all well and good, but behavioral segmentation groups them by usage patterns and the actions they take with your product. Hey, I might discover that taskflow actually has three distinct user types. Maybe we have the power organizers, those who use every feature. They set up complex templates, they track their time, they generate detailed reports. They want even more advanced capabilities. And then maybe we've got the group of, like, simple trackers. They just want basic project management. You know, they're creating tasks and setting deadlines, checking things off. In fact, they find most features overwhelming. They just want clear project visibility. And maybe our third group is the collaborative communicators who live in the comments and tag people. They treat it like a. Like Slack, more like a communication tool than a true project management tool. And even at a single marketing agency, they might have all three of these types of users. And they're probably frustrated with taskflow for completely different reasons. The power organizers, they want more features. And the simple trackers, they want fewer features. And the collaborative communicators, they want better notification controls. Whatever the case may be, without behavioral segmentation, you might never realize you're trying to serve three different products to three different types of customers. All right, so there you go. There's three techniques you can try out with your LLM. One, pattern recognition, Two, sentiment analysis, and three, behavioral segmentation. Using these techniques, you might uncover patterns that completely flip your understanding of customer success. And this matters because most product decisions, well, they're based on incomplete pictures of customer behavior. And when you understand the real patterns behind customer success and failure, you stop wasting time building the wrong things and start focusing on what actually drives results.
