What it is
Analysis of behavioral usage data in tools such as LogRocket, Pendo, FullStory, or Amplitude to find trends, friction, and drop-off. It shows what happens at scale, not why.
Why use it
Analytics show the size and spread of behavior across the real user population, which small samples cannot, and where qualitative research is most needed.
Use this when
- Behavioral data exists and you need to understand usage
- You want to see where users drop off or struggle at scale
- You need to know if a suspected problem is widespread or isolated
- You are starting research and deciding where to focus
You may not need this when
- The feature is new, with little usage data
- The question is about motivation or context, not volume
- Instrumentation does not cover the relevant flows
Dependencies — what you need
- Access to analytics tools (e.g., LogRocket, Pendo, FullStory, Amplitude)
- Questions defined before pulling data
- Enough usage data to show patterns
People involved
- Design
- Product
- Analytics
- Data Science
Typical outputs
- Quantified drop-off points and friction areas
- Feature adoption and usage frequency
- Patterns from sampled recordings and heatmaps
- Hypotheses for qualitative follow-up
Template
Analytics Review template
A fill-in structure for running this method: what to prepare, what to do, what to record, and how to read the result.
How AI can help
AI as augmentation — not replacement
- Help write exploration queries or filters
- Summarize your notes on recordings or heatmaps into a findings note
- Suggest questions about why, to test with research
- Draft a findings brief, using only the numbers supplied
AI prompt
A prompt for any AI assistant. It works only from material you paste in, keeps assumptions apart from findings, and leaves the decisions to you.