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Leave out anything that does not help test your most important assumptions or deliver the core value you are trying to validate. Start by deciding what you need to learn; then choose the least costly experiment that can produce useful evidence. The right MVP might be a landing page or a manually operated service—not working software.
Decide what the MVP needs to teach you
Before debating features, write down the consequential uncertainty your release or experiment should resolve. A useful learning question is specific enough to guide a test: for example, “Will small retailers pay for a simpler way to reconcile online orders?” is more actionable than “Do people like this idea?”
Then define what observation would change your decision. Specify what would count as evidence to continue, reshape the offer, pivot, or run another test. Planning backward from what the team needs to learn and measure is the core of Strategyzer’s experiment-planning guidance.
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List assumptions as separate, testable statements rather than bundling them into a feature wish list. Consider whether the idea is desirable to customers, feasible to deliver, viable as a business, and adaptable as circumstances change. Ask which assumption, if wrong, would most seriously undermine the idea, and how strong the available evidence is. Strategyzer recommends mapping assumptions by importance and evidence strength to identify what to test first: assumption mapping.
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- Desirability: Do the intended customers have the problem, and does the proposed value matter to them?
- Feasibility: Can the team deliver the experience with its available capabilities and resources?
- Viability: Can the product or service work as a sustainable business?
- Adaptability: Can the idea respond to what is learned or to meaningful changes in its context?
Prioritize assumptions that are both important and weakly supported. A low-risk detail with strong evidence usually deserves less immediate attention than a core customer or delivery assumption that could invalidate the idea.
Choose the experiment before choosing features
The smallest useful test depends on the uncertainty. A landing page can test response to a proposition; a storyboard or video can communicate a proposed experience; a clickable or working prototype can test interaction or technical feasibility. A Wizard of Oz setup can test a service while people perform work behind the scenes that software might later automate. Strategyzer’s experiment methods describe choosing tests to learn about specific assumptions.
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These formats answer different questions. A response to a landing page is evidence of interest in the proposition, not proof that users will adopt, keep using, or pay for a finished product. A prototype may show that an interaction is understandable or technically possible without establishing business viability. Match the method to the hypothesis and be explicit about what its result can and cannot establish.
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What to leave out—and what not to cut
Defer work that does not change the learning or provide the core proposition needed for a meaningful test. Depending on the experiment, that may include visual polish, secondary workflows, broad integrations, automation, or edge cases. These are practical candidates for deferral, not a universal list: if an integration is central to the customer’s choice, for instance, removing it may make the test misleading.
Do not cut away the thing being tested. A product intended for actual use or sale still needs enough value for target users to understand and choose it, enough usability for them to figure it out, and enough feasibility for the team to deliver it with available resources. Marty Cagan’s product-focused definition of an MVP emphasizes those conditions: SVPG on the minimum viable product. That is distinct from using “MVP” to mean an experiment whose purpose is to test a hypothesis; state which meaning you intend.
Compare plausible features with evidence
When two or more features compete for inclusion, compare them by how directly they test a critical assumption, whether they preserve the value or preference being investigated, and what it will cost and take to learn. Also check whether the remaining experience will be usable and feasible. These checks help keep scope tied to the decision rather than to a target feature count.
Customer preferences and priorities can be explored through collaborative exercises or split tests, methods discussed in Strategyzer’s guide to testing customer preferences. A stated preference is not the same as observed behavior, so use the method and evidence standard that fit the decision. An A/B test can show which option people chose without necessarily explaining why; follow-up discovery may be needed.
Keep the result interpretable
A negative response can be ambiguous if the test combines an uncertain customer problem with an uncertain solution. Users may reject the proposed interface, the value proposition, or the problem framing itself. Strategyzer recommends separating assumptions about customers and their jobs, pains, and gains from assumptions about the proposed value proposition: Value Proposition Canvas guidance.
Choose research methods for the question they can answer. Usability testing can reveal whether people complete tasks, but it does not by itself show that they value or will buy the solution. Surveys can gather responses, but asking people what to build is not a substitute for observing meaningful behavior. SVPG discusses these discovery limitations and the risks of relying on working product as the prototype: SVPG on product discovery.
After the test, record what happened, which assumption the result bears on, what remains uncertain, and the next decision. A useful MVP is not the build with the fewest features; it is the smallest credible way to learn what matters without removing the value or clarity needed to make the evidence meaningful.
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