Ran Experiments → 2.4 Wins/Mo: Growth PM Bullets
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Growth PMs are graded on cadence + win-rate + compounding impact. The rewrite quotes all three. Generic AI writes 'ran experiments' and stops · junior tier.
Ran 38 onboarding experiments in 11 months · 27 conclusive, 14 wins (2.4/mo cadence) · compounding to a 22% lift in 30-day activation.
What changed and why
- Experiment cadence > experiment count. 2.4 wins/month is the unit growth orgs benchmark on.
- Win-rate (14/27 = 52%) is the rigour signal · too high suggests p-hacking, too low suggests bad hypotheses.
- Compounding lift > single-test lift. 22% activation over 11 months is the cumulative story recruiters trust.
- Quote the area (onboarding, checkout, paywall) · generic AI never picks the right surface.
Recruiter perspective
“2.4 wins/month at 52% conclusion-to-win is a real growth org cadence. I'm interested.”
Related rewrites
Owned Roadmap → Killed 3 Bets, Shipped 4: Product Manager Bullets
Replaced 'multiple cross-functional teams' (the laziest PM AI phrase of 2026) with budget, team count, the kill-vs-ship ratio, and one outcome from the highest-impact bet. PMs who can quote a kill-rate are senior; PMs who only quote ships are juniors.
Launched Features → 64% WAU Adoption: PM Launch Bullets
Two PM AI clichés removed ('drive adoption', 'improve experience metrics'). Replaced with the feature name (scheduled-send), the cohort metric (% WAU), the time-to-adoption, and one downstream usage metric only the owner could pull.
Built Models → +£1.1M ARR From Churn Model: Data Science Bullets
Three vague nouns ('predictive models', 'customer outcomes', 'business strategy') replaced with the algorithm class, AUC, the operational workflow it landed in, the save-rate delta, and the £ARR attribution. Data scientists who can quote ARR attribution outrank those who only quote AUC.