View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Product Strategy DIBB |  Growth Flywheel |  Product Discovery Experiment Canvas |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | Growth often breaks down where a reinforcing mechanism builds up too much friction. A Flywheel shows the cycle of value, repetition, and reinforcement meant to sustain growth. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | Low | Medium | Low | High |
Timedifferent | 1-2 h | 60-120 min | 30-60 min | 1-4 Wochen |
Participantsdifferent | 2-8 | 3-8 | 1-5 | 1-6 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Flywheel Map, Friction Points, Growth Levers, Experiment Backlog | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | GrowthRetentionConversion | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |