Question 1

What is the difference between product analytics and web analytics?

Accepted Answer

Web analytics (Google Analytics) tracks pageviews and traffic sources. Product analytics (PostHog, Mixpanel) tracks user actions within your product—button clicks, feature usage, conversion funnels. You likely need both, but product analytics drives product decisions.

Question 2

Should I use Google Analytics or something else?

Accepted Answer

Google Analytics is great for marketing (traffic, campaigns, SEO). For product insights, use a product analytics tool like PostHog, Mixpanel, or Amplitude. They track user journeys, not just pageviews.

Question 3

What events should I track?

Accepted Answer

Start with your core user journey: signup, key feature usage, conversion events, and churn signals. Track the actions that indicate success. Avoid tracking everything—focus on decisions you need to make.

Question 4

How do session recordings help?

Accepted Answer

Session recordings show exactly what users do—where they click, scroll, hesitate, and leave. Watch recordings of users who convert and those who don't to understand the difference. Invaluable for finding UX issues.

Question 5

What are heatmaps used for?

Accepted Answer

Heatmaps show aggregate behavior: where users click, how far they scroll, what they hover over. Use them to optimize page layouts, identify ignored elements, and ensure key content is seen.

Question 6

How do I set up conversion funnels?

Accepted Answer

Define the steps to your goal (e.g., Landing → Signup → Onboarding → First Value). Your analytics tool will show conversion rates between steps and where users drop off. Focus improvements on the biggest drop-off points.

Question 7

How do I track without violating user privacy?

Accepted Answer

Use privacy-respecting tools (PostHog self-hosted, Plausible, Fathom). Disclose tracking in your privacy policy. Anonymize data where possible. Consider cookie-less tracking options. Comply with GDPR/CCPA as applicable.

Question 8

What is cohort analysis?

Accepted Answer

Cohort analysis groups users by when they signed up (or another attribute) and tracks their behavior over time. It reveals whether recent users behave differently than early users—essential for measuring product improvements.

Question 9

How do I identify power users vs. casual users?

Accepted Answer

Define engagement criteria (daily active, feature usage, session length). Segment users by these metrics. Study what power users do differently. Try to move casual users toward power user behaviors.

Question 10

What metrics indicate product-market fit?

Accepted Answer

High retention (users coming back), strong engagement (frequent, deep usage), organic growth (referrals), and low churn. The "40% very disappointed" survey is another PMF indicator. Track these religiously.

Question 11

Should I build analytics into my MVP?

Accepted Answer

Yes, from the start. You need data to validate your MVP works. Keep tracking simple initially—core conversion events and basic usage. Sophistication can come later when you have more users and questions.

Question 12

How often should I review analytics?

Accepted Answer

Daily: quick glance at key metrics. Weekly: deeper review of trends and funnels. Monthly: comprehensive analysis and strategic decisions. Set up alerts for significant changes to catch issues quickly.

Question 13

What is the difference between qualitative and quantitative data?

Accepted Answer

Quantitative is numbers (analytics). Qualitative is words (user feedback, interviews). Use quantitative to find what is happening, qualitative to understand why. Both are essential—numbers without context lead to wrong conclusions.

Question 14

How do I A/B test effectively?

Accepted Answer

Test one variable at a time. Wait for statistical significance. Have a clear hypothesis. Measure impact on your north star metric, not just the changed element. Most tools (PostHog, Optimizely) have built-in A/B testing.

Question 15

What should I do when data contradicts my intuition?

Accepted Answer

Trust the data, but verify it is correct first. Check for tracking bugs, segment biases, or missing context. If data is solid, update your intuition. This is how you develop product sense—through validated learning.

I need to understand how users behave

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