Product analytics tools show you what users do. Revenue tools show you what they pay. But when they’re disconnected, you can’t answer the question that matters:
Which features drive retention and expansion?
If your stack is Amplitude (events), Postgres (user data), Stripe (billing), and Snowflake (warehouse), here’s how to connect behavior to business outcomes.
The 3 questions product analytics should answer
1. Feature adoption → retention
Feature adoption to retention analysis reveals whether users who adopt specific features stay longer, validating which features drive value. It identifies which features predict churn, helping you intervene before customers leave. Most importantly, it calculates retention lift by feature usage, showing the actual impact of each feature on customer retention.
2. Feature adoption → expansion
Feature adoption to expansion analysis shows whether power users upgrade more frequently, validating feature value. It identifies which features drive upsells, helping you prioritize development. Most critically, it calculates expansion rate by usage tier, showing how feature usage correlates with revenue growth.
3. Onboarding → activation
Onboarding to activation analysis identifies the fastest path to activation, helping you optimize onboarding flows. It reveals which onboarding steps matter most for getting users to their “aha moment”. Most importantly, it shows where users drop off before they pay, highlighting critical conversion barriers.
What to build first (week 1)
Start with a simple feature → revenue analysis:
- Feature usage (from Amplitude, by user and feature)
- User data (from Postgres, plan, signup date, cohort)
- Billing data (from Stripe, MRR, churn, expansion)
- Unified view (join on user ID, calculate retention and expansion by feature)
Once you have this unified view, you can answer critical questions: Which features actually drive retention? Which features drive expansion and upsells? What’s the ROI of building feature X, helping you prioritize product investments?
Why product analytics tools aren’t enough
Product analytics tools are great at showing behavior but not great at showing business impact because revenue data lives elsewhere in Stripe or billing systems. User context lives elsewhere in Postgres or CRM systems, making it hard to connect behavior to business outcomes. Historical analysis is limited with basic cohort analysis and trends. Most critically, segmentation is basic, only allowing filtering by plan, cohort, or feature without deeper insights.
When you connect product events to revenue, you can prioritize features that actually drive retention rather than just measuring usage. You can identify features that predict churn, allowing proactive intervention. Most importantly, you can measure the ROI of product investments, ensuring you build features that drive business value.
The hidden cost of disconnected analytics
When product and revenue analytics are separate, you build features that don’t move retention because you can’t see which features actually drive customer value. You miss features that predict churn because you can’t connect behavior to outcomes. Most critically, you can’t measure the ROI of product work, making it impossible to prioritize effectively.
Connected analytics shows which features matter by linking behavior directly to revenue outcomes, so you can build what actually drives growth rather than what’s easy to measure.