Hypothesis design, A/B discipline and a learning repository that lasts

Hypothesis

Design that survives scrutiny

We start with the user job and the funnel friction — not with a button colour randomiser. Strong hypotheses cite qualitative signals, prior data or competitive prompts. Weak hypotheses cite vibes. During briefing workshops we kill weak ideas early so production capacity goes to tests that could change spend or roadmap decisions.

Each hypothesis names the customer segment, the expected mechanism (“reduce form fields → lower abandonment among mobile first-timers”), and the secondary metrics we refuse to break (margin, support load, brand safety). That framing keeps stakeholders honest when a “winner” secretly harms retention.

A/B & holdouts

Tests your team can trust

Where traffic allows, we prefer randomised A/B or multivariate designs with QA gates before launch. Where traffic is thin, we design sequential tests, geo or cohort holdouts, or qualitative pairing — and we say so clearly so nobody pretends a soft launch is science. Sample size and runtime estimates are shared before kickoff, along with stop rules if a variant is harmful.

Holdouts matter when you scale a channel or automate a lifecycle. Running everything “on” for everyone makes learning impossible. We protect control groups carefully, especially in email and paid retargeting ecosystems where contamination is common.

Repository

Learning that outlasts campaigns

Every closed experiment receives a one-page note: context, design, result, decision, next questions. Notes live in a shared repository tagged by funnel stage and channel. New marketers inherit a map instead of folklore. This is the quiet advantage of working with GrowthStrike — the loop compounds even when people change roles.