Launch Metrics Template for Product Teams

Track 5-8 primary metrics across the five categories. Each metric should answer a specific question about the launch. If you cannot explain what decision the metric will inform, drop it. Vanity metrics (page views, impressions) rarely inform product decisions.

For new features, you will not have direct baselines. Use proxy baselines instead: product-level error rates, comparable feature adoption curves from past launches, or industry benchmarks. Document your assumptions so you can validate them after launch.

Use three inputs: past launch performance (what did similar features achieve?), beta results (if applicable), and market benchmarks. When in doubt, start conservative. A 15% adoption target that you beat feels better and leads to better decisions than a 40% target you miss. The [Product Analytics Handbook](/analytics-guide) has frameworks for benchmarking.

At the 30-day checkpoint, compare actuals to the failure threshold you defined upfront. If metrics are below the failure line and the trend is flat or declining, call it. Investigate root causes before deciding next steps: the feature might need better onboarding, not a rebuild.

Yes. Quality metrics (error rate, latency, tickets) should have strict thresholds that trigger immediate action. Product metrics (adoption, engagement) should have aspirational targets that inform strategy. A 2x error rate is a crisis. A 50% miss on adoption is a learning opportunity.