Your Customers Are Telling You Something. Are You Listening?

Imagine a digital product that works perfectly: no application errors, no infrastructure issues and no unusual response times. Yet conversion is falling. Customers start an application, interact with forms and then leave. Technical dashboards show no obvious reason why.

The explanation may lie not in the technology, but in the way people experience and use the product. Modern observability therefore needs to go beyond whether a service works and help organizations understand where users struggle — and what may be behind their behaviour.

The problem isn't always technical

Consider an online loan application. The platform loads quickly and every form works as expected, but customers still abandon the process. They may not understand a question, hesitate when asked for additional information or feel uncertain about the way costs are presented. These situations do not necessarily trigger technical alerts, yet they can directly affect business results.

Traditional analytics shows where users leave a conversion funnel. Session recordings reveal what happened during an individual visit. Understanding recurring reasons behind abandonment requires analysing behaviour at scale.

The journey from observed behaviour to AI-supported insight.

From thousands of sessions to meaningful insights

Digital products generate large volumes of behavioural data: clicks, form interactions, selections, navigation steps and session recordings. Reviewing every recording manually is not realistic when thousands of sessions are generated each day.

Large Language Models (LLMs) can help translate complex interactions into structured descriptions of individual journeys. Rather than focusing on isolated clicks, the analysis looks at three elements:

• User intent - what the customer was trying to achieve.

• Observed behaviour - what the customer actually did.

• Friction points - where the customer hesitated, encountered difficulty or abandoned the process.

This creates a bridge between raw interaction data and information that product, UX and business teams can use.

From a session recording to a structured customer story.

Finding patterns across customer journeys

A structured summary of one session is useful, but the real value emerges when similar behaviours can be identified across many sessions. AI-supported behavioural clustering can group journeys according to recurring interaction patterns — even when those patterns were not defined in advance.

For example, analysis may reveal repeated hesitation around employment-status fields, repeated changes to loan parameters or prolonged reviews of cost information before users leave the process. These patterns help teams focus their investigation on specific parts of the journey.

AI-supported discovery of recurring behavioural clusters.

From patterns to evidence-backed action

A useful behavioural insight should go beyond a label. It should provide three things:

• A research hypothesis - what might be happening?

• Behavioural evidence - which repeated interactions support the hypothesis?

• An actionable recommendation - what should the team investigate or test next?

For instance, repeated reviews of cost details before abandonment may suggest that customers need clearer information about additional costs. A product team could review the presentation of costs, conduct UX research and test an alternative design.

A behavioural cluster can be translated into a hypothesis, evidence and a recommendation.

AI supports better decisions. It does not replace human expertise.

AI-generated insights should be treated as research hypotheses, not unquestionable conclusions. Observed behaviour may support a hypothesis about user intent, but it does not prove motivation or causality. Product specialists, UX researchers, business teams and technical experts provide the context needed to assess findings, prioritise improvements and design experiments.

AI does not replace A/B testing. It can make experimentation more focused by helping teams identify which issues deserve attention first. Human teams decide what to change; experiments help establish whether the change improves the outcome.

AI helps prioritise what to test; human teams remain responsible for decisions.

The future of observability is also about understanding people

By combining technical telemetry with behavioural data and AI-supported analysis, organizations can better understand how customers use digital products, where they encounter friction and what may prevent them from completing their goals.

The value is not in generating more dashboards or processing more data. It is in discovering meaningful patterns, asking better questions and turning evidence into improvements. Sometimes the biggest problem in a digital product is not a system failure — it is something customers have been trying to tell us through their behaviour.

Watch the Full Presentation

Want to explore the topic further and see how AI can uncover hidden patterns in user behaviour? Watch Sławomir Michalik's full presentation on YouTube to discover the approach to AI-powered customer behaviour analysis in more detail.

https://youtu.be/s1Mm_-3m4RY?si=XE82fJF6PhNJpH2p

Sławomir Michalik