Validate the customer’s problem and your riskiest assumptions before investing in a full build. Start with evidence about what a specific customer needs, choose a small test that can challenge your idea, and decide in advance what result would justify the next investment. Validation reduces uncertainty; it cannot guarantee a successful business.
What validation can—and cannot—tell you
Software-idea validation is a sequence of evidence-gathering decisions made before substantial implementation. It helps you learn whether a target customer has a meaningful problem, whether your proposed solution addresses it, and whether the idea merits further investment. Microsoft Learn’s startup validation module likewise focuses on customer value, testing assumptions, and interviewing customers.
Validation is not a prediction that a product will succeed. It reduces specific risks so you can decide what to test or invest in next. A favorable response to a concept does not establish that the product is feasible to build or that its business model can work.
Start with a customer and a problem
Write down who you believe has the problem and what happens in their current situation. Keep the description specific enough that you could recognize the intended first customer in a conversation. Then investigate how they deal with the problem now: existing software, manual workarounds, a different service, or doing nothing.
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Ask about recent behavior rather than leading with your product pitch. Useful questions include:
- When did this problem last occur, and what were you trying to accomplish?
- What did you do to handle it? What tools or workarounds did you use?
- What makes the current approach difficult, costly, or acceptable?
- Who else is involved in choosing or paying for a solution?
- What would have to change for you to try a different approach?
The European Commission Joint Research Centre (JRC) report Agile product discovery: A methodology for product innovation describes discovery questions about pain, potential need alleviation, first customers, adoption criteria, and alternatives—including existing habits. Those questions help reveal whether the problem is consequential to the people you hope to serve.
Separate evidence for the problem from evidence for the solution
Do not treat enthusiasm for a feature as proof that the underlying problem exists. Make two hypotheses explicit:
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- Customer-problem hypothesis: A defined group encounters a particular problem in a recognizable situation.
- Problem-solution hypothesis: A proposed solution addresses that problem in a way that changes a measurable behavior or outcome.
Test the first before relying on the second. Grace Ng’s Lean Enterprise Institute article, “Why Lean Startup Experiments are Hard to Design,” emphasizes testing assumptions rather than assuming the founder’s preferred solution is what customers need.
Choose the riskiest assumption to test first
List what must be true for the idea to work. For example, the customer must experience the problem often enough to care; the proposed approach must fit their workflow; they must be willing to adopt or pay for it; and your team must be able to deliver it. Prioritize the assumption that is both central to the idea’s viability and least supported by evidence.
That priority matters: testing a convenient detail while leaving a critical uncertainty untouched can create confidence without reducing the risk that could invalidate the idea. Ng’s article recommends testing the riskiest assumption first and making experiments measurable.
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Match the experiment to the uncertainty
Choose the smallest test that could give useful evidence about the assumption. Interviews, landing-page tests, and manual concierge delivery are examples of relatively low-cost early experiments described by Ng. The JRC report also discusses questionnaires, mockups, and limited pilots. These are options, not a required checklist; each answers different questions.
| Method | Useful for testing | What the evidence does—and does not—show |
|---|---|---|
| Customer interviews | Whether a problem occurs, how people handle it, and what influences adoption. | People’s accounts and examples can clarify behavior and pain; compliments or hypothetical intent alone do not establish that they will adopt or pay. |
| Landing-page test | Whether a concrete offer earns a response from the intended audience. | Observed responses are more informative than general praise, but interest in a page is not the same as product use or a completed purchase. |
| Manual concierge delivery | Whether a service delivered by people can produce value before you automate it. | It can reveal delivery needs and user response, but does not by itself prove that software automation is technically feasible or economically viable. |
| Questionnaire | Structured questions about needs, priorities, or reactions across a group. | Answers describe what respondents report; the method does not alone establish future behavior. |
| Mockup | Whether people understand or respond to a proposed interface or workflow. | Reaction to a representation can inform design and offer questions, but is not evidence of a working product’s performance. |
| Limited pilot | Whether a constrained version or service works with users in a relevant setting. | It can provide behavioral evidence in that setting; conclusions still depend on how closely pilot users and conditions match the intended use. |
Compare candidate tests by the question they answer, how closely participants match your first customer, what people actually do versus merely say, the cost and time required, and whether the result could change your next decision. A simple experiment is useful only if it tests an important uncertainty.
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Write down the weakest result that would justify taking on more risk. Choose a measure that matches the hypothesis and test: for example, a defined action by the right audience or a specific piece of evidence about how they handle the problem. State what result would count against the idea, too.
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There is no universal interview count, conversion threshold, or deposit target established for validating every software idea. The appropriate criterion depends on the customer segment, the assumption, and the decision you are trying to make. Ng writes, “Defining what success should look like is the most crucial step before conducting an experiment.” Setting the rule in advance makes it harder to move the goalposts after seeing an encouraging result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Assess customer value, feasibility, and business viability separately
Before committing to a substantial build, consider three different risks. The JRC report treats these as distinct parts of product discovery:
- Customer value: Does the target customer consider the problem important enough to adopt or buy a solution? The JRC’s validation questions include reactions to an MVP, interest in use, a demo or pilot, willingness to buy and at what price, and which characteristics matter.
- Technical feasibility: Can your team build and operate the product with the skills and resources available? Customer interest does not answer this question.
- Business viability: Can the product and its revenue model support a viable business? Positive concept feedback does not settle the economics.
A test that supports one category does not settle the others. Treat each as an assumption to investigate rather than letting a promising result in one area stand in for evidence across all three.
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Use the evidence to continue, revise, or stop
Compare what happened with the criterion you wrote before the experiment. Make the next decision explicit:
- Continue testing: If the riskiest assumption meets the criterion, move to the next consequential uncertainty. That may mean testing a different customer group, validating a delivery approach, or investigating feasibility.
- Revise: If results challenge the assumption, reconsider the customer, the problem, or the solution. Write a revised hypothesis and choose a test that can distinguish it from the earlier one.
- Stop or defer: If the evidence fails the criterion and no plausible revision warrants another test, avoid committing to a larger build on the strength of the original idea alone.
Changing direction when an assumption fails is part of learning, not a failure of the process. The Lean Enterprise Institute describes adjusting strategy when assumptions do not hold; the JRC frames product discovery as reducing risk. For a program-specific example of customer value as a basis for adoption and growth, the U.S. National Science Foundation’s Project Pitch information describes NSF criteria; those criteria should not be treated as universal startup rules.
Further reading
The JRC report identifies Eric Ries’s Lean Startup methodology as the basis for the product-discovery approach it describes. The Lean Startup is optional further reading, not a prerequisite for testing a software idea.
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