Common Sources of User Behavior Data

User behavior data comes from analytics events, interaction recordings, surveys, interviews, support conversations, and business records. Each source answers a different question. Choose the evidence you need by deciding whether you want to measure a pattern, inspect an interaction, hear a user's explanation, or confirm a business outcome.

For a step-by-step investigation method, read our user behavior analysis guide. This article focuses on where the evidence comes from and what each source can establish.

Analytics events and journey data

Page views, screen views, clicks, form interactions, and journey steps show what was recorded and how often it occurred. Compare completed and abandoned journeys to locate a step worth investigating.

Check collection coverage before comparing periods or platforms. Consent, event definitions, identity rules, and implementation changes can affect the result. A missing event does not by itself prove that the user abandoned the task.

Heatmaps, visit recordings, and Experience Metrics

Heatmaps summarize recorded interactions. Visit recordings let you inspect the sequence around an observed pattern. Experience Metrics highlight interactions worth checking, including rage clicks and dead clicks.

These sources help you form and examine hypotheses. A click location does not establish what a person noticed, and repeated clicks do not establish their emotion or the technical cause. Start with a relevant segment and review visits that match it. See the Experience Metrics overview for the signals available in CUX.

Surveys, interviews, and usability tests

Ask people about their expectations, difficulties, and reasons for a decision. A short survey can capture feedback at a particular step. An interview can explore the context behind a choice. A usability test lets you observe someone attempting a defined task and ask follow-up questions.

Treat stated reasons as evidence to compare with observed behavior. The people who respond may differ from those who do not, and a test setting may change how participants behave. These methods help explain experiences; they do not measure how common each experience is across all visitors.

Support conversations and customer records

Support tickets, reviews, CRM records, and purchase history connect reported problems with customer context. A recurring payment complaint can help you choose a journey to investigate. Order records can confirm whether a purchase succeeded when the analytics event is missing.

Check timestamps, definitions, and identity matching before joining records. People who contact support are a selected group. Their reports can identify important problems without representing the experience of every customer.

Web and mobile application data

Websites and mobile apps are environments in which you collect evidence. The source might be an interaction event, an app error, a recording, feedback, or a transaction.

For mobile journeys, check whether screen definitions, app versions, and collection coverage are comparable. If your product spans web and mobile, use a documented identity approach before treating activity on both platforms as one person's journey. CUX supports web and mobile analysis; the mobile analytics overview explains its mobile offering.

Experiment results

An A/B test generates evidence about a defined change under its assignment and measurement conditions. Journey data and relevant recordings can help you investigate how participants interacted with each version.

A before-and-after comparison alone does not isolate the effect of a change. Traffic, timing, measurement, and other releases may also differ. Use the A/B testing guide when an experiment is appropriate for your decision.

Choose sources that answer your question

For a checkout completion drop, start with journey data and order records to check the outcome and locate the affected step. Use relevant recordings and support reports to examine possible barriers. If you need to understand expectations that were not observable, ask users directly.

Choose sources that can challenge your explanation as well as support it. Write down what each source measures, which users it covers, and what remains unknown. Our guide to combining quantitative and qualitative data explains how to bring those forms of evidence together.

In CUX, Business Goals, Waterfalls, user journeys, Experience Metrics, and visit recordings support this investigation. AI-assisted interpretation can help with an initial reading, while Digital Experience Analysts can review the evidence and guide the next decision. Feedback, CRM, and transaction records remain separate sources to bring into your investigation where relevant.