Discover · Core
Use data to find where users struggle
Data shows you what users do and where they give up. Use it alongside research: data tells you where the problem is, and talking to users tells you why.
About 2-5 daysEvergreenLast reviewed 5 November 2026
Who this applies to
- Skills it builds
- Data and measurementDiscovery and research
- PM types
- All
- Product types
- All
- Starting points
- All
- Ways of working
- All
- Experience
- All
- Size of organisation
- All
Steps, by experience
Beginner (under 1 year)
- Pick the user journey linked to your learning questions, for example signing up, completing an order or submitting a form.
- Find out what data already exists: product analytics, support tickets, complaints, reviews, survey comments and operational reports. Ask an analyst or engineer where it lives.
- Build a simple funnel: list each step and how many people reach it. Look for the step where the most people drop out.
- Split the numbers by groups that might behave differently, such as new and returning users, device type or region.
- Read 30 to 50 support tickets or comments for the worst step and note the common themes.
- Write down three to five 'data signals' with numbers, and the questions they raise for user research.
Intermediate (1 to 4 years)
- Build a funnel for the target journey and find the largest drop-off in absolute numbers.
- Segment by user type, device and channel to find where the problem is concentrated.
- Triangulate with support tickets, reviews and survey text.
- Summarise signals with numbers and feed the open questions into research.
Experienced (5+ years)
- Combine behavioural, qualitative and operational data to size each pain point by users affected and value at stake.
- Check data quality and tracking gaps before trusting a pattern; note confidence for each signal.
- Look for cohort and trend changes, not just a snapshot, to separate new problems from old ones.
- Use the signals to aim research at the highest-value questions and to set baselines for later measurement.
Who can do this
Owner: Product manager
Can be delegated to
- Data Analyst or Performance Analyst or Data Scientist / ML Engineer: Build the funnel and segment the data
- Backend Engineer or Tech Lead: Explain what is tracked, where the data lives and any gaps
- User Researcher: Analyse support tickets and comments for themes
Reviewed by: Tech Lead, User Researcher
Tool examples
- Google Analytics 4
- Use a funnel exploration to see drop-off between each step of the journey, then add a breakdown by device category.
- Amplitude
- Build a funnel chart and click on the worst step to see what people did instead.
- Mixpanel
- Create a funnels report and compare conversion for new and returning users.
- Power BI
- Combine analytics exports with support ticket counts on one page so you can see both together.
- Claude
- Paste anonymised support ticket text and ask it to group the tickets into themes with a count for each. Remove any personal details first.
Definition of done
- Funnel or step data for the target journey, with the biggest drop-off identified
- Key segments compared
- Three to five data signals written up with numbers and confidence
- Open questions handed to user research
Common pitfalls
- Trusting numbers without checking how they are tracked
- Looking at percentages only and missing where most people are actually lost
- Treating data as the answer to 'why' rather than 'where'
If you lead the team
What good looks like: Teams use data to find where users struggle, then use research to find out why.
Ask your PM
- What does the data show about where users drop off?
- Which of these signals have we checked with real users?
Warning signs
- Decisions rest on a single chart with no context.
- Data and research tell different stories, and no one has noticed.