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Criterion
Paper illustration of Pretotyping with its method-specific working model.
Product Discovery
Pretotyping
A paper-based illustration representing Design Sprint with its core stages and visible working result.
Innovation
Design Sprint
Paper illustration of Outcome-Driven Innovation with its method-specific working model.
Product Discovery
Outcome-Driven Innovation
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
Purposedifferent
Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data.When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess.Outcome-Driven Innovation clarifies customer problems, solution opportunities, and evidence. It separates jobs, desired outcomes, importance, satisfaction, and opportunity, and captures results as desired outcome statements, an opportunity landscape, and segments by underserved needs.When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small.
Complexitydifferent
LowHighHighMedium
Timedifferent
Stunden bis wenige Tage4-5 TageMehrere Wochen1-5 Tage
Participantsdifferent
1-45-82-6 researchers plus sampleNutzertraffic
Formatdifferent
Workshop + asyncWorkshopWorkshop + asyncAsync
Outputdifferent
Pretotyping sketch, Test setup, Conversion data, Go or no-go decisionPrototype, Test Findings, Decision RationaleDesired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypothesesClick Data, Interest Signal, Learning Decision
Tagsno overlap
ValidationDemandMVP
Design sprintPrototypeValidationInnovation
OutcomesInnovationResearchQuantitative
ValidationExperimentsDemandDiscovery
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