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| Criterion |  Growth Funnel Analysis |  UX Research Diary Study |  Growth Pirate Metrics AARRR |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. | When experiences build up over days or weeks and a single session cannot capture them, Diary Study records the course of everyday life. Recurring triggers, moods, and habits become visible this way, beyond the sharpness of memory. | 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 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 | Medium | Medium | Medium | High |
Timedifferent | 1-3 h | 1-4 Wochen | 1-2 h Setup, laufend | 1-4 Wochen |
Participantsdifferent | 1-5 | 5-20 | 2-8 | 1-6 |
Formatdifferent | Async | Async | Workshop + async | Async |
Outputdifferent | Funnel report, Drop-off analysis, Optimization hypotheses | Diary Entries, Longitudinal Patterns, Experience Timeline | AARRR funnel, Metric baseline, Experiment backlog | Experiment results, Decision log, Learning summary |
Tagsno overlap | AnalyticsConversionGrowth | UX researchTrackingBehavior | GrowthMetricsExperiments | ExperimentsGrowthAnalyticsValidation |