Product Validation
Test product assumptions through focused experiments and agreed criteria before deciding to build, change direction, or test further.
Why?
Validation helps you decide what the evidence supports now. We define assumptions and decision thresholds before testing, distinguish stated interest from observed behavior, and keep uncertainty visible in the recommendation.
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Demand signals you can inspect
See how target users responded and which behaviors support the hypothesis.
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A decision before a larger build
Use agreed evidence thresholds to decide whether to proceed, change direction, or test again.
Our process
Define testable assumptions
We identify the riskiest demand, usability, and value assumptions and agree on measures and decision thresholds.
Prepare a focused experiment
We choose a prototype or landing-page experiment appropriate to the question and define its limitations.
Test with target users
We observe tasks and reactions, capture consented feedback, and distinguish payment intent from actual commitments.
Analyze experiment evidence
We examine agreed metrics, sample quality, and alternative explanations rather than relying on a single headline rate.
Review proceed or revise criteria
We compare results with the original thresholds and recommend proceeding, revising, stopping, or gathering more evidence.
Hand over the validation record
We document methods, results, limitations, and next experiments so your team can review the basis of the decision.
Questions?
User research explores needs and behavior. Product validation tests specific assumptions about a proposed product against agreed decision criteria. It does not by itself prove product-market fit.
We agree on the prototype or experiment needed to answer the question. Its fidelity, tasks, and boundaries are scoped in advance; it is not a production-ready product.
We define relevant actions such as completing a task, signing up, or making a commitment. We report the sample and context, and distinguish stated willingness to pay from actual payment.
We explain which assumptions were challenged and why. The recommendation may be to revise, stop, or run another experiment where evidence is inconclusive.
