Democratize experimentation with a centralized platform and self-serve tooling; reset baselines regularly.

Scale experimentation with AI: use Cursor desktop/cloud agents for parallel builds and visual QA; orchestrate docs/analysis via Claude; automate cleanups and reporting.

Test “obvious” UX changes; preserve helpful friction and align with user mental models.

Build capabilities, not just tests—use experiments to unlock platform features (e.g., metering, paywalls).

Make experimentation company-wide: centralize data (BigQuery), broadcast wins/losses in Slack via GrowthBook, and auto-correlate metric dips to releases.

Build growth loops from habits: design shareable artifacts and personalized signup paths; drive users back to your domain to capture value.

Ship first, then optimize: launch PLG features and immediately run experiments to increase adoption; track daily active usage per feature.

Test big levers—not just UI: pricing models, usage limits, onboarding pathways—and judge success by ARR movement, not micro-metrics.

Prioritize by risk: run rigorous A/B tests where you have volume; use before/after or non-inferiority for low-risk in-product changes.