Quick Answer (TL;DR)
Every PM needs three analytics capabilities: funnel analysis (where users drop off), cohort analysis (how retention changes over time), and feature usage tracking (what users actually use). The specific tool matters less than having these three views.
Why This List Matters
Data-informed PMs ship better products. But "data-informed" does not mean "drowning in dashboards." It means knowing which metrics to watch, when to dig deeper, and how to connect product analytics to product decisions. These 10 tools and metrics give you that capability.
1. Activation Rate Tracking
Best for: Measuring whether new users reach your product's core value
Activation rate is the most important early metric. If users do not activate, nothing downstream matters. Read the Activation Rate guide for how to define and measure your product's activation moment. Track Time to Value alongside it.
2. Feature Adoption Rate
Best for: Measuring whether users actually use the features you build
Shipping features nobody uses is the most expensive form of waste. Feature Adoption Rate tells you what percentage of users engage with each feature. Use it to decide what to double down on and what to sunset.
3. A/B Testing
Best for: Measuring the causal impact of product changes
Correlation is not causation. A/B tests are the only way to know whether a change actually caused a metric improvement. Use the A/B Test Calculator for sample sizing and the A/B Testing guide for best practices.
4. Cohort Retention Analysis
Best for: Understanding how retention changes over time and across user groups
The Cohort Retention Curve reveals whether your product has a retention floor (users who stay forever) or continuous decay. Segment by acquisition channel, feature usage, or onboarding completion to find what drives retention.
5. Funnel Analysis
Best for: Finding where users drop off in key workflows
Map your critical paths (signup to activation, trial to paid, feature discovery to usage) and measure conversion at each step. The biggest drop-off is your biggest opportunity. Track Onboarding Completion Rate as a key funnel metric.
6. DAU/MAU Stickiness
Best for: Measuring how frequently users return to your product
The DAU/MAU ratio is a simple, powerful engagement metric. Rising stickiness means your product is becoming more essential to users. Falling stickiness is an early warning sign.
7. Revenue Analytics (MRR/ARR)
Best for: Connecting product changes to revenue outcomes
Track how features impact MRR and ARR. Use the MRR Calculator for quick math. Segment by New MRR, Expansion MRR, and Churned MRR to understand the composition of growth.
8. Click-Through and Engagement Metrics
Best for: Measuring how users interact with specific UI elements and content
Click-Through Rate and Scroll Depth reveal whether users see and engage with your content and features. Low CTR on a feature entry point means users do not know it exists or do not care.
9. North Star Metric Dashboard
Best for: Keeping the team aligned on the single most important metric
Every product team needs one metric they check daily. Use the North Star Finder to identify yours. Then build a dashboard that shows the North Star, its input metrics, and trends.
10. Customer Health Scoring
Best for: Predicting churn before it happens using behavioral signals
Composite scores combining usage frequency, feature breadth, support interactions, and NPS. The Customer Health Score guide explains how to build and use a health score to intervene before at-risk customers leave.
How We Ranked These
Tools are ranked by decision impact (how directly they inform product decisions), PM accessibility (whether PMs can use them without analyst support), and versatility (whether they apply across product types). Activation and feature adoption rank highest because they are the most direct measures of product value.