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| Criterion |  UX Research HEART Framework |  Product Strategy Counter Metrics |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | When a headline metric could mask side effects, it adds guardrail signals against unwanted outcomes. It connects customer value, product logic, and decision priorities. The result is captured as a counter-metric list and guardrail definitions. | 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. | 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 | Low | Low | High |
Timedifferent | 120 min initial, dann laufend | 30-60 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-6 | 2-6 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | HEART-GSM Table, Dashboard | Counter Metric List, Guardrail Definitions | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | MetricsUX researchMeasurementSatisfaction | MetricsMeasurementStrategyExperiments | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |