By the end of this lesson you can
- Use product analytics to find design problems and opportunities.
- Read funnels, cohorts and event data.
- Combine analytics with qualitative research.
The idea
Product analytics tools record what people do: pages viewed, buttons clicked, steps completed. Used well, they show where problems are. Qualitative research then explains why.
Common analyses
- Funnels: how many people move through each step of a process, and where they drop off.
- Cohorts: groups of users who started at the same time or share a trait, compared over time (for example retention).
- Feature adoption: how many people use a feature and how often.
- Paths: common routes people take.
- Segments: comparing groups, such as mobile and desktop or new and returning users.
Event tracking
Designers can help define which events to track, with clear names such as checkout_delivery_selected. Plan tracking alongside the design, not after launch.
Cautions
- Analytics show behaviour, not intent: a long time on page could mean interest or confusion.
- Respect privacy: collect only what you need, follow consent rules and avoid tracking sensitive data.
- Beware vanity metrics that look good but do not reflect value.
Why it matters in real projects
Analytics help designers find the biggest problems, prioritise work and build evidence-based arguments. They reveal patterns across thousands of users that no interview could show. Combined with qualitative research, they lead to confident, targeted design decisions.
A realistic example
A fitness app's funnel shows 70% of new users finish onboarding, but only 20% log a workout in their first week. Segmenting shows Android users drop off much more than iOS users.
The designer runs five quick interviews with Android users and discovers that the workout logging button is hidden behind a floating menu that overlaps the system navigation on many Android phones. A layout fix raises first-week logging among Android users significantly.
Step by step
Define questions
Write what you want to learn from data.
Build a funnel
Map key steps and find the largest drop-off.
Segment
Compare devices, new and returning users and other groups.
Form hypotheses
Write possible reasons for patterns.
Investigate why
Use interviews, recordings or tests.
Plan tracking
Define events for your next design.
Common mistakes
Mistake Assuming you know why from numbers alone.
Instead Investigate with qualitative research.
Mistake Tracking everything.
Instead Track what answers your questions and respect privacy.
Mistake Celebrating vanity metrics.
Instead Focus on measures linked to value.
Check your understanding
Choose an answer, then check it. There is an explanation either way.
Practice exercise
Exercise · about 45 min
Funnel analysis
Analyse a funnel (use sample data or your own product).
Steps
- Map the steps and conversion rates.
- Find the largest drop-off.
- Write three hypotheses.
- Plan a qualitative study to test them.
Deliverable
A funnel diagram, hypotheses and a research plan.
Self-assessment
Tick each item when it is true of your exercise. Be honest; this is for you.
4 items
Summary
- Analytics show where problems are.
- Use funnels, cohorts, adoption, paths and segments.
- Plan event tracking with design.
- Combine with qualitative research and respect privacy.
Further reading
Third-party sources, chosen for reliability. They open in a new tab.
- Analytics and user experience (opens in a new tab)Nielsen Norman Group
- Measuring success: using data (opens in a new tab)GOV.UK Service Manual
Your notes
Marking complete updates your course and roadmap progress.