Feature Experiment Template for Product Growth

Use a feature experiment when the feature could materially affect a core product metric (activation, retention, revenue) or when you are uncertain about the right design. Ship directly when the change is low-risk, the user benefit is obvious, and the worst-case outcome is that nobody uses it. Bug fixes, copy changes, and minor UI improvements rarely need full experiments. The [Product Analytics Handbook](/analytics-guide) discusses when statistical rigor matters and when shipping fast is the better strategy.

Phase 0 (internal): 3-5 business days. Phase 1 (beta): 5-7 days, enough for qualitative feedback. Phase 2 (limited rollout): 7-14 days, enough for statistical significance on your primary metric. Phase 3 (broad): 7-14 days, to confirm the result holds at scale. Total experiment duration is typically 4-6 weeks.

Do not ship. A feature that improves collaboration but degrades page load time or increases errors will hurt retention long-term. Fix the guardrail issue first, then re-run the experiment. The only exception is if the guardrail degradation is within a pre-defined acceptable range that you documented before launch.

Build your own only if you have very simple needs (boolean on/off for a few features). For percentage rollouts, user targeting, and experiment tracking, use a feature flag service (LaunchDarkly, Statsig, Eppo, Flagsmith). The cost of a tool is far less than the engineering time to build and maintain a reliable system yourself.

At most companies, 20-30% of features go through formal experiments. The rest are shipped based on strong user research signals, competitive necessity, or low-risk improvements. The features worth experimenting on are the ones where you are genuinely uncertain about the outcome and the metric impact is material. ---