Analyzing Customer Behavior To Drive Business Growth

A lot of us still rely on simple trend lines or linear regression when analyzing how user behavior changes over time. But in recent years, the tools available to us have evolved significantly. For behavioral and UX data - especially when it's noisy, nonlinear, or limited - there are now better methods to uncover meaningful patterns. Machine learning models like LSTMs can be incredibly useful when you’re trying to understand patterns that unfold across time. They’re good at picking up both short-term shifts and long-term dependencies, like how early frustration might affect engagement later in a session. If you want to go further, newer models that combine graph structures with time series - like graph-based recurrent networks - help make sense of how different behaviors influence each other. Transformers, originally built for language processing, are also being used to model behavior over time. They’re especially effective when user interactions don’t follow a neat, regular rhythm. What’s interesting about transformers is their ability to highlight which time windows matter most, which makes them easier to interpret in UX research. Not every trend is smooth or gradual. Sometimes we’re more interested in when something changes - like a sudden drop in satisfaction after a feature rollout. This is where change point detection comes in. Methods like Bayesian Online Change Point Detection or PELT can find those key turning points, even in noisy data or with few observations. When trends don’t follow a straight line, generalized additive models (GAMs) can help. Instead of fitting one global line, they let you capture smooth curves and more realistic patterns. For example, users might improve quickly at first but plateau later - GAMs are built to capture that shape. If you’re tracking behavior across time and across users or teams, mixed-effects models come into play. These models account for repeated measures or nested structures in your data, like individual users within groups or cohorts. The Bayesian versions are especially helpful when your dataset is small or uneven, which happens often in UX research. Some researchers go a step further by treating behavior over time as continuous functions. This lets you compare entire curves rather than just time points. Others use matrix factorization methods that simplify high-dimensional behavioral data - like attention logs or biometric signals - into just a few evolving patterns. Understanding not just what changed, but why, is becoming more feasible too. Techniques like Gaussian graphical models and dynamic Bayesian networks are now used to map how one behavior might influence another over time, offering deeper insights than simple correlations. And for those working with small samples, new Bayesian approaches are built exactly for that. Some use filtering to maintain accuracy with limited data, and ensemble models are proving useful for increasing robustness when datasets are sparse or messy.

Surveys can serve an important purpose. We should use them to fill holes in our understanding of the customer experience or build better models with the customer data we have. As surveys tell you what customers explicitly choose to share, you should not be using them to measure the experience. Surveys are also inherently reactive, surface level, and increasingly ignored by customers who are overwhelmed by feedback requests. This is fact. There’s a different way. Some CX leaders understand that the most critical insights come from sources customers don’t even realize they’re providing from the “exhaust” of every day life with your brand. Real-time digital behavior, social listening, conversational analytics, and predictive modeling deliver insights that surveys alone never will. Voice and sentiment analytics, for example, go beyond simply reading customer comments. They reveal how customers genuinely feel by analyzing tone, frustration, or intent embedded within interactions. Behavioral analytics, meanwhile, uncover friction points by tracking real customer actions across websites or apps, highlighting issues users might never explicitly complain about. Predictive analytics are also becoming essential for modern CX strategies. They anticipate customer needs, allowing businesses to proactively address potential churn, rather than merely reacting after the fact. The capability can also help you maximize revenue in the experiences you are delivering (a use case not discussed often enough). The most forward-looking CX teams today are blending traditional feedback with these deeper, proactive techniques, creating a comprehensive view of their customers. If you’re just beginning to move beyond a survey-only approach, prioritizing these more advanced methods will help ensure your insights are not only deeper but actionable in real time. Surveys aren’t dead (much to my chagrin), but relying solely on them means leaving crucial insights behind. While many enterprises have moved beyond surveys, the majority are still overly reliant on them. And when you get to mid-market or small businesses? The survey slapping gets exponentially worse. Now is the time to start looking beyond the questionnaire and your Likert scales. The email survey is slowly becoming digital dust. And the capabilities to get you there are readily available. How are you evolving your customer listening strategy beyond traditional surveys? #customerexperience #cxstrategy #customerinsights #surveys

More CX programs are being cut, and the reason is painfully clear. Proving the impact of customer experience is easy when you look across industries. Studies from Watermark Consulting, Forrester, the Qualtrics XM Institute, and others consistently show that CX drives business growth. But here’s the catch: Your executives don’t care about cross-industry stats. They care about YOUR company, YOUR customers, and how CX impacts YOUR bottom line. The good news? It’s absolutely possible to connect the dots—and we’ve done it for our clients. The key lies in uncovering how changes in customer behavior—like growing their business with you—tie back to your CX data. Take an insurance company and its agents as an example. There’s always variation: some agents are growing their business with you, while others are shrinking. The question is: why? Here’s where CX data becomes invaluable. Don’t just rely on high-level metrics like NPS or overall satisfaction. Dig deeper into your driver questions and text analytics to uncover what sets the growing customers apart from those who are stagnant or leaving. For instance, we helped one insurance company discover that agents who reported issues with the commission process (not the amount, but the process) were far more likely to shrink their business or leave altogether. In a manufacturing company, we identified that customers with unresolved complaints placed significantly fewer future orders. The truth is that CX is directly linked to business value, but it’s up to us to prove it. This requires more than survey data. You need to integrate financial, behavioral, and operational data to reveal the full picture. Once you do, you can demonstrate the impact of CX and take meaningful action to drive growth. CX isn’t optional. It’s the difference between companies that thrive and those that stagnate. Let’s make sure your organization understands that. #CX #customerexperience #ROI #CXROI