Innovation Process model
Reduces product uncertainty through Build-Measure-Learn, minimum viable products, and innovation accounting.
Lean Startup states leap-of-faith assumptions, builds the smallest learning-capable MVP, measures behaviour against a baseline, and decides persevere, pivot, or stop.
What smallest experiment creates decision-ready learning about the riskiest assumption?
Lean Startup uses Build-Measure-Learn loop with MVP, actionable metrics, and pivot/persevere decision. Assessments remain justified and context-bound.
Hypothesis
Name the value or growth hypothesis and riskiest assumption.
Minimum viable product
Build the smallest offer that elicits real behaviour.
Measure
Analyse baseline, cohort, and actionable metric.
Decide
Decide persevere, pivot, or stop with rationale.
Update after new evidence or a material context change and retain the previous version.
For new offers with high value or growth uncertainty.
validated learning loop with traceable sources, assumptions, and domain decisions.
Owners, dates, and next review for validated learning loop.
Fictional worked example
Build-Measure-Learn loop with MVP, actionable metrics, and pivot/persevere decision as a precise working structure with a fictional example.
Hypothesis information to record
Teams zahlen 180 €/Monat für Auditexport.
MVP information to record
Concierge MVP mit fünf Teams.
Metric/threshold information to record
≥3/5 LOIs nach zwei Reviews; Ergebnis 1/5.
Pivot/persevere information to record
Segmentproblem pivotieren.
The example demonstrates the domain logic of Lean Startup; worksheets remain blank.
Start with the core question: What smallest experiment creates decision-ready learning about the riskiest assumption?
For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight.
The approach revolves around hypotheses whose risk is deliberately tested early instead of being elaborated mentally. Small experiments deliver feedback about behavior, willingness to pay, or usage and thereby sharpen the picture before a lot of effort flows into a large solution. What matters is the learning loop between assumption, measurement, and adaptation.
Lean Startup works because it puts assumptions in contact with the market and makes learning the actual unit of progress. The method is more than a buzzword only when hypotheses, measurement logic, and decision criteria are visible before the experiment. Especially important are high-risk core assumptions, honest evaluation, and the tangible consequence drawn from what was learned, so that small tests are really allowed to change direction in the business model.
A recurring learning rhythm in which hypotheses, experiments, and decisions are looked at together is suitable for anchoring the practice. A sensitive signal is present when activity is mistaken for insight or when measurements do not support a real decision. Close every cycle with a clear learn, a conclusion, and the next risky assumption to be tested.
The method guide walks execution through 4 phases, timing, 4 pitfalls, and clear stop criteria.
Open method guideYour perspective
Your feedback helps shape the next improvements to methodatlas.
Plan Lean Startup around its domain steps and the concrete decision question.
To be confirmed
To be confirmed
Full initiative Time: 1–4 weeks per loop
Complete this worksheet on paper or in your own document during the work.
Hypothesis information to record
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MVP information to record
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Metric/threshold information to record
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Pivot/persevere information to record
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Enter only real input, sources, and decisions.
Expected artifact: leap-of-faith assumption
leap-of-faith assumption · source · uncertainty · next decision
Entry:
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Expected artifact: MVP
MVP · source · uncertainty · next decision
Entry:
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Expected artifact: innovation accounting
innovation accounting · source · uncertainty · next decision
Entry:
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Expected artifact: learning decision
learning decision · source · uncertainty · next decision
Entry:
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Method guide: https://methodatlas.meierhoff-systems.de/en/methods/lean-startup/run-sheet
Fitting alternatives with decision guidance, followed by other related methods.
Instead of Lean Startup, choose Assumption Mapping when you need to clarify before running experiments which assumptions are critical, uncertain, and testable first.
Instead of Lean Startup, choose Experiment Canvas when you want to clearly prepare an experiment with goal, hypothesis, measurement, and risk.
Moves customer problem, solution idea, and evidence toward a concrete result through "list assumptions", "map assumptions to dimensions", and "derive the test plan".
Separates hypothesis, observation, insight, and next action into four fields after a test.
Condenses hypothesis, test, metric, and success threshold onto one card before execution.
A fast venture-style triage that tests whether an idea has a clear problem, paying customer, differentiated solution, and plausible market.
