Build an MVP by first naming a customer and a consequential problem, then testing the riskiest assumption with the least costly experiment that can produce credible evidence. That experiment may be a landing page, prototype, or manually delivered service—not necessarily a stripped-down software product. Decide in advance what result would change your next step, observe what customers do as well as what they say, and use the evidence to continue, revise, or test again.
What an MVP is—and what it is not
A minimum viable product is a learning instrument: a way to test an important assumption about a value proposition or business model with minimal effort. It is not automatically a smaller version of the finished product, and “minimum” does not mean removing features arbitrarily. The right MVP is the fastest credible way to learn whether a consequential assumption holds.
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Eric Ries, quoted in Strategyzer’s MVP guidance, describes it as “the fastest way to get through the Build-Measure-Learn feedback loop with the minimum amount of effort.” The practical implication is to work backward from the question you need answered, rather than forward from a feature list.
How to plan an MVP test
1. Define the customer and problem
Be specific about who experiences the problem, when it occurs, and what outcome that person needs. Distinguish users from buyers and decision-makers: they may be the same person, but they may not be. A test with people who like an idea but cannot choose or pay for it may not answer whether the business can work.
In discovery conversations, ask about the customer’s jobs, pains, and desired gains before presenting your proposed solution. Questions about a recent real situation—what the person did, what was difficult, and what happened next—can surface context and correct assumptions. Structured artifacts or prioritization activities can help compare responses. Strategyzer’s customer-interview guidance discusses this discovery approach.
2. List assumptions and choose the riskiest one
Write down what must be true for the idea to work, then group the assumptions into three categories:
- Desirability: Will the intended customers want, choose, or use this solution?
- Feasibility: Can the team deliver it technically and operationally?
- Viability: Can the business create value sustainably relative to its costs?
Choose the assumption whose failure would most change the plan. Testing an easy but low-impact detail first can consume time without reducing the uncertainty that matters.
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3. Select the smallest credible experiment
Work backward from the learning goal: specify what evidence would answer the question, choose a measure that captures that evidence, and then create only the artifact or service needed to produce the observation. Strategyzer lists examples such as a value-proposition data sheet, brochure, storyboard, landing page with a clear call to action, mock product box, explanatory video, basic learning prototype, or a Wizard-of-Oz MVP. In a Wizard-of-Oz test, a product-like front end is presented to customers while people handle some of the work manually behind the scenes.
A proxy can be faster and cheaper than building the intended product. A landing page may test whether a clearly described offer earns signups; a manual service may test whether customers value an outcome before automation exists. Use a functional prototype when the question depends on interaction or technical performance that a mockup cannot demonstrate.
4. Set the decision rule before running the test
Record the hypothesis, customer segment, action or outcome to observe, and threshold for support, rejection, or uncertainty. For example: “Among [defined segment], at least [chosen threshold] will take [specific action] after seeing [specific offer] within [defined test period].” Choose the threshold for your own decision and constraints; it is not a universal benchmark.
Keep the strength of evidence in view. Interviews and surveys tell you what people say. Reactions to an artifact show how they respond to a presented idea. Signups, purchases, and presales show actions or commitments, although the action’s cost and meaning still matter. In a September 2, 2026 article, Strategyzer describes levels 1–2 of its evidence scale as what people say and levels 3–5 as what people do. That is Strategyzer’s framework, not a universal scientific standard.
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5. Interpret the result and choose what happens next
Compare the result with the assumption and the threshold you set. If evidence is ambiguous, revise the experiment or clarify the assumption instead of calling the idea validated. If results contradict the assumption, consider changing the customer, problem, value proposition, or business model. A negative result can still be useful when the test genuinely examined the intended question; a confusing offer, mismatched participants, or technical failure can make a weak result hard to interpret.
Choosing an experiment that fits the question
There is no universal best MVP format. Compare plausible options by asking what each can reveal, how much it costs in time and effort, and how clearly you could interpret a disappointing result.
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| Experiment | Useful for | What it can establish—and its limits |
|---|---|---|
| Customer interview | Understanding context, vocabulary, current workarounds, and perceived pains or gains | Rich stated evidence; by itself, interest in a conversation does not establish future action or purchase. |
| Landing page with a clear call to action | Testing response to an offer or message with a defined audience | Can reveal whether visitors take the chosen action; results depend on who sees the page and how the offer is presented. |
| Storyboard, brochure, video, or mockup | Testing comprehension and reactions before building a product | Produces responses to an artifact, not proof that people will use or pay for a finished product. |
| Learning prototype | Testing an interaction or a technical question that static materials cannot answer | Can expose usability or feasibility issues; requires more work than a lightweight artifact. |
| Concierge or Wizard-of-Oz service | Testing whether customers value a service outcome while delivery is partly manual | Can test the experience without full automation; manual delivery does not itself prove that automated delivery will be feasible or viable. |
For any option, check whether participants match the intended user, chooser, or payer; whether the observed action is meaningful; and whether failure would distinguish lack of demand from a confusing presentation, poor recruitment, or a technical problem. A low-cost test is only useful when it can answer the question you actually have.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What customer interviews can—and cannot—tell you
Interviews are valuable for learning how people describe a problem and what they currently do about it. They are less conclusive when the question is whether someone will act or pay: stated enthusiasm and actual commitment are different evidence.
Strategyzer reports a specific 2026 Canadian consumer packaged-goods customer-profile example involving 20 interviews. In that team’s case, interviewees deleted at least once 91% of the assumed customer-job statements because they said those statements did not apply; 55% of the pains the team assumed were not true for those customers; and 75% of interviewees commented on at least one gain statement, refining or correcting the team’s understanding. These are case-specific observations, not expected rates or benchmarks for other teams. Strategyzer describes the example and method.
The lesson is not that a particular number of interviews guarantees insight. Use conversations to discover and refine the problem, then choose an experiment that observes the behavior or commitment your next decision depends on.
Common MVP mistakes to avoid
- Starting with features. A feature list does not identify the uncertainty that could invalidate the idea.
- Calling a small release an MVP by default. A working app can be an expensive way to test a question a mockup or manual service could answer.
- Treating compliments as validation. Record what people actually do, not only what they say they might do.
- Changing the success threshold after seeing results. A pre-set decision rule makes it harder to reinterpret weak evidence as success.
- Testing with the wrong people. Responses from people outside the intended customer or buyer segment may not resolve the relevant assumption.
- Overclaiming from one experiment. A result supports or challenges a particular assumption under particular conditions; it does not prove an entire business model.
How to adapt the process to the stakes
Do not assume there is a standard MVP budget, duration, sample size, or feature count. These depend on the customer segment, uncertainty, experiment, and consequences of being wrong. Set and disclose your own constraints and threshold rather than borrowing a number without context.
For regulated, safety-critical, or capital-intensive products, a lightweight market experiment may not be enough to address the risks involved. The test must account for the relevant domain constraints and evidence needs; the methods described here do not establish specific compliance requirements.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchFor further guidance, Strategyzer’s Understanding customers course covers discovery interviews, assumption mapping, and experiment selection. Its page displayed USD $400 when reviewed; price and availability can change, so check the current page. Strategyzer’s MVP article also points to Value Proposition Design for deeper guidance; buying a book is not a prerequisite to running an MVP test.
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