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What “validation” can—and cannot—tell you
Software validation is a sequence of evidence-based decisions made before committing to substantial implementation. It helps you decide whether to continue, change direction, or stop; it does not guarantee that a product or business will succeed. Microsoft Learn’s startup validation module centers customer value and testing assumptions with customers.
Keep three risks distinct: whether customers value the product, whether your team can build and deliver it, and whether the business can be financially viable. The European Commission Joint Research Centre’s report, Agile product discovery: A methodology for product innovation, treats these as separate questions. A favorable reaction to a concept may be useful evidence of interest, but it does not establish technical feasibility or a sustainable business model.
1. Identify a customer and a real problem
Describe a specific kind of person or organization and the situation they face. Avoid starting with a feature list. A useful first statement is: “For [specific customer], [situation] causes [observable difficulty], and today they handle it by [current behavior].” It is a hypothesis to investigate, not a claim to treat as proven.
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Ask how people deal with the issue now: which tools they use, what workarounds they have adopted, what they pay for, or why they do nothing. The JRC report’s customer-discovery questions include pain, existing alternatives and habits, possible ways to alleviate the need, likely first customers, and adoption criteria. These details help distinguish an urgent problem from one that sounds plausible in the abstract.
2. Separate the problem from your proposed solution
First test whether the intended customer actually experiences the problem. Then test whether your proposed solution addresses it in a way that matters. These are different hypotheses: a real problem does not prove that your preferred product is the right answer.
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Grace Ng, co-founder of Javelin.com, makes this distinction in the Lean Enterprise Institute article “Why Lean Startup Experiments are Hard to Design.” The article advises teams to formulate hypotheses, test assumptions, and make experiments measurable. Start with customer behavior and context rather than asking people to endorse a feature you have already decided to build.
3. List assumptions and test the riskiest one first
Write down what must be true for the idea to work. Assumptions may concern the severity of the problem, access to the intended customer, willingness to change habits, interest in the proposed outcome, delivery, or revenue. Prioritize the assumption that is both important to viability and least supported by evidence. Testing a low-impact detail first can consume time without resolving whether the idea deserves investment.
For each assumption, identify what observation could support or contradict it. If the concern is whether a specific group experiences a recurring problem, conversations about recent behavior may be more useful than a polished mockup. If the uncertainty is whether people will take a concrete next step for an offer, a landing-page test may be more informative than additional discussion.
4. Choose the smallest experiment that answers the question
Choose a method based on the uncertainty you need to reduce, not because every startup is supposed to follow the same checklist. Ng describes interviews, landing-page tests, and manual concierge delivery as low-cost options. The JRC report also discusses questionnaires, mockups, and limited pilots. Each method offers different evidence.
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| Method | Useful for testing | What to watch for |
|---|---|---|
| Customer interviews | Whether a problem exists, how it affects people, and how they handle it today | Concrete accounts of past behavior and workarounds are more informative than compliments or guesses about future use. |
| Landing-page test | Response to a specific offer or description | Define what action counts as a meaningful response; page visits alone do not establish adoption or willingness to pay. |
| Manual concierge delivery | Whether users value an outcome when you provide it without automating the service | Separate evidence of value from whether the process can later be delivered efficiently at scale. |
| Questionnaire | Structured feedback across a defined group | Answers are reported preferences; they are not equivalent to observed behavior or a purchase. |
| Mockup | Reactions to a proposed workflow or product characteristics | Interest in a representation does not by itself show that people will adopt or pay for the product. |
| Limited pilot | Use, delivery, and adoption questions in a constrained real-world setting | Specify what the pilot can establish and what remains untested, including broader feasibility or financial viability. |
Compare options by the question they answer, the strength of the evidence, cost and time, how closely participants match the intended first customer, and whether the result could change your next decision. A direct conversation or manual test can be quicker than a polished prototype, but neither answers every question. Do not mistake an enthusiastic response for a purchase: the JRC report specifically includes questions about interest in use, a demo or pilot, willingness to buy and at what price, and which product characteristics matter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Set a decision rule before collecting evidence
Write down the weakest result that would justify taking on more risk, and choose a measure that fits the hypothesis and experiment. The rule should be specific enough to guide a decision, but grounded in your customer segment and context. For example, a test might ask whether a defined group takes a particular next step after seeing a concrete offer; the required level of response should be set for that test, not borrowed as a universal benchmark.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsNg writes, “Defining what success should look like is the most crucial step before conducting an experiment.” Setting the criterion first makes it harder to reinterpret disappointing results as success after the fact. The sources do not establish a universal interview count, conversion rate, or deposit target for software ideas; choose a context-specific minimum rather than treating an arbitrary number as proof.
6. Assess value, feasibility, and business viability separately
- Customer value: Does the intended customer see enough benefit to adopt, use, or buy the proposed solution? What behavior supports that conclusion?
- Technical feasibility: Does your team have the skills, resources, and practical means to build and deliver what the offer requires?
- Business viability: Is there a plausible way for the product and its revenue model to support the costs and ongoing operation?
These questions are related, but evidence for one does not settle the others. Someone may want an outcome that is difficult for your team to deliver, or a technically workable product may not support a viable business. Before a substantial build, identify which risk remains unresolved and what evidence would reduce it.
7. Decide whether to continue, revise, or stop
Compare what happened with the decision rule you wrote before the test. If the key assumption is supported, move to the next important uncertainty rather than treating the result as proof of the whole business. If evidence contradicts it, revise the customer, problem, or solution hypothesis and run a test suited to the revised question. If the idea no longer warrants investment, stopping is a valid outcome.
The Lean Enterprise Institute describes changing strategy when an assumption fails; the JRC report presents product discovery as a way to reduce risk. In either case, the purpose is to make a better next investment decision—not to secure a promise of success. Eric Ries’s The Lean Startup is optional further reading on the methodology identified as a basis for the JRC report’s product-discovery approach; it is not a prerequisite for running these tests.
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