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| Criterion |  Growth A/B Testing |  UX Research HEART Framework |  Growth Pirate Metrics AARRR |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | 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. | Helps clarify observations, needs, and patterns in concrete terms. It groups observations into patterns, questions, and decisions. The result is captured as a HEART-GSM table and dashboard. | For products with complex usage paths, overall growth alone is too coarse to reveal bottlenecks. Pirate Metrics breaks the relationship with the product into consecutive stages and shows where the funnel actually leaks. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | High | Medium | Medium | Low |
Timedifferent | 1-4 Wochen | 120 min initial, dann laufend | 1-2 h Setup, laufend | 1-5 Tage |
Participantsdifferent | 1-6 | 3-6 | 2-8 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | HEART-GSM Table, Dashboard | AARRR funnel, Metric baseline, Experiment backlog | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | MetricsUX researchMeasurementSatisfaction | GrowthMetricsExperiments | ValidationExperimentsDemandGrowth |