Problem → Hypothesis → Measurement Template

This interactive template helps teams move from a loosely stated problem to a testable hypothesis and a measurable plan. Use it to clarify who will act on the result, what success looks like, what data you need, and the method you'll use. Save responses so plans can be revisited, copied, and connected to experiment tracking.

Filled example (Customer churn)

Problem: Monthly active users who contact support are churning at a higher rate than average.
Decision owner: Head of Customer Success
Desired outcome: Reduce 90-day churn for contacted users from 12% to 8% within 3 months.
Candidate hypothesis: If we proactively offer a dedicated onboarding session to contacted users, then their 90-day churn rate will decrease.
Leading signals: Signup for onboarding session, reduction in support repeat contacts within 30 days.
Lagging signals: 90-day churn rate, net revenue retention.
Success criteria: 90-day churn drops to <= 8% and effect is statistically significant (p < 0.05) with minimum detectable effect of 3 percentage points.
Required datasets: user_id, contact_date, support_contact_reason, onboarding_session_attendance, churn_flag (90-day), revenue; daily export, owned by CS analytics.
Suggested method: Randomized experiment (A/B test).
Estimated effort: ~120 person-hours.
Risks: Selection bias if attendance is voluntary; mitigate with incentivized attendance and intention-to-treat analysis.