Product analytics examples: 8 questions, analyses and decisions

Product analytics is easiest to understand through examples. This article walks through eight: an onboarding funnel, a retention predictor, feature adoption, a sudden drop explained by one app version, a navigation detour, at-risk accounts, a pricing funnel blocked by payment method, and power-user concentration. Each shows the question, the analysis, a sample result and the decision it led to.

What does product analytics look like in practice?

Product analytics is the analysis of how people use a digital product (which features they use, in what order, how often, and whether they stay) to make better product decisions. The definitions are easy; what helps is seeing concrete questions turned into analyses and decisions. Below are eight product analytics examples, each with the question, the analysis, a sample result and the action it led to.

All numbers are sample data. The point is the pattern: question → the right analysis → a decision you could not have made without the data.

Eight product analytics examples

1. Finding the onboarding step that loses users (funnel)

Question: Why do only 35% of sign-ups create their first project? Analysis: Funnel from sign-up → workspace named → template chosen → first project, by device. Result: 40% of mobile users drop at "template chosen"; the template gallery is unusable on small screens. Action: A mobile-first template picker. First-project rate on mobile rose from 24% to 37%.

2. Which feature predicts retention (retention by behavior)

Question: What do retained users do in their first week that churned users do not? Analysis: Week-4 retention of users who did vs did not use each feature in week one. Result: Users who invited a teammate in week one retained at 58% vs 21% for those who did not. Action: An invite prompt after the first project. (Correlation, so the team tested it: invites rose and retention followed.)

3. Did the new feature get adopted? (feature adoption)

Question: Is the new "recurring tasks" feature used? Analysis: Share of weekly active users using it, by plan, over six weeks. Result: 4% overall, but 19% on the Business plan, where recurring work is common. Action: Feature highlighted in Business onboarding instead of a general announcement.

4. Why did activity drop on Tuesday? (attribution of a change)

Question: Daily active users fell 9% on one day. Analysis: Break the change down by platform, version and country. Result: 85% of the drop came from Android version 5.2.0. Action: A crash in 5.2.0 found and fixed in a hotfix the same day.

5. How users really navigate (paths)

Question: How do users get from the dashboard to exporting a report? Analysis: Path analysis forward from the dashboard. Result: Most users go dashboard → settings → reports, a detour through settings. Action: An export button on the dashboard; median time to export fell from 3 minutes to 40 seconds.

6. Who is about to churn? (lifecycle and segments)

Question: Which accounts are drifting away? Analysis: Segment of accounts active last month, with active days down by more than half this month. Result: 140 accounts, 30% of them on annual plans renewing within 60 days. Action: Customer success outreach to those accounts first.

7. Is pricing page interest turning into upgrades? (conversion by segment)

Question: Free users visit pricing; why so few upgrade? Analysis: Funnel pricing view → plan selected → checkout → paid, split by company size property. Result: Teams of 10+ select a plan but stop at checkout, which requires a credit card; many need invoices. Action: Invoice billing for annual plans. Upgrades from 10+ teams doubled.

8. Are power users different? (distribution)

Question: How concentrated is usage? Analysis: Distribution of active days per user per month. Result: 12% of users are active 20+ days a month and create 61% of content. Action: A power-user program and early access to advanced features for this group.

The analyses behind the examples

Every analysis should end with a decision.

How to start with your own examples

1. Write down five questions your team argues about.
2. For each, decide which analysis answers it and which events you need.
3. Check the events exist and are correctly named; add missing ones.
4. Run the analysis, write the result in one sentence, and agree on the action.
5. Re-run it after the change ships.

Common mistakes

• Starting from data instead of questions. Dashboards full of charts rarely change decisions.
• Treating correlation as cause. Validate with an experiment or a staged rollout.
• Ignoring segments. Averages hide the group where the action is.
• Analyses without actions. If no decision follows, the analysis was not needed.

Running these analyses in TapCub

TapCub's product analytics covers each analysis above: funnels, retention reports, lifecycle, user paths, event distributions, segments, and the AI assistant's "why did it change" attribution by platform, version, country, channel or page. Events come from autocapture, custom events and server-side events; see create a tracking plan.

Related: product usage analytics, feature adoption, what does a product analyst do.

FAQ

What is an example of product analytics? Measuring where new users drop out of onboarding with a funnel, then fixing the step that loses the most users and checking the funnel again.

What questions can product analytics answer? Where users struggle, which features drive retention, who is likely to churn, how users navigate, and whether a release helped or hurt.

What data do I need? Events for key actions with properties (plan, platform, version), user and account identity, and a consistent naming convention.

How is product analytics different from web analytics? Web analytics focuses on traffic and page views. Product analytics focuses on what identified users do inside the product over time.

Sources

• Google Research. The HEART framework.
• Kohavi, R. et al. Trustworthy Online Controlled Experiments. Book site.