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What an MVP should—and should not—do
A minimum viable product is a focused product experience that lets a team learn from a real test. “Minimum” is relative to the question being tested; “viable” means the intended user can experience the proposed core value. The Google News Initiative advises starting with the simplest or most important user problem for the experiment, rather than trying to test every part of a business at once (Google News Initiative Startups Playbook).
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It is not necessarily a polished, market-ready product, nor is it automatically a rough demo. Microsoft for Startups distinguishes a prototype or demo—which may be controlled or incomplete—from an MVP intended to support actual users on real infrastructure. Some definitions frame an MVP as revenue-ready, but terminology varies; that is not a universal requirement (Microsoft for Startups MVP guide).
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Stage | Purpose | Core journey and conditions | Expected breadth and polish |
|---|---|---|---|
| Prototype | Explore an idea or test reactions to a proposed experience. | May use simulated screens or controlled conditions; users may not complete a real end-to-end service. | Enough fidelity to explore the question; not necessarily a working product. |
| MVP | Test important assumptions with a focused, usable experience. | Intended users can experience the core value under the conditions the test requires. | Only the scope needed for the learning goal and safe, reliable operation. |
| Market-ready product | Compete and serve customers beyond a narrow learning test. | Designed to handle broader real-world use. | More complete features and polish may be necessary. Boundaries vary by product and definition. |
The distinctions are practical rather than universal standards. Choose the label that clarifies what the test can actually demonstrate.
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How do you decide what to build first?
1. Name the user, problem, and learning goal
Write a single test statement before scoping features: “For [specific user] with [specific problem], we believe [proposed value]; we will learn whether this is true by observing [behavior or feedback] during [small test].” Keep the user specific enough to recruit and observe. If the person who pays is different from the person who uses the product, name both: Microsoft for Startups recounts founder Lindsey Goodchild using customer-discovery sessions to explore feature screens and notes that purchaser and end user can differ (Microsoft for Startups).
Then list only assumptions that matter to this test. Depending on the idea, these may concern whether the problem is important, whether people can use the proposed solution, whether they will adopt it, whether the team can deliver it, or whether someone will pay. Do not treat every uncertainty as a reason to build a feature.
2. Identify the riskiest assumption
Ask which unproven belief would most undermine the product if it were false. Microsoft for Startups recommends identifying core value and potentially undermining assumptions before coding, so the first product can test them directly (Microsoft for Startups MVP guide).
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For example, if the key uncertainty is whether a user can understand a new workflow, a detailed usability test may matter more than a payment system. If the uncertainty is whether a business will pay, a test that only records positive interview feedback may not answer it. Match the experiment to the uncertainty rather than building a broad feature set by default.
Choose the smallest experiment that can answer the question
A full software release is only one option. Pick the least complex approach that can produce credible evidence about the riskiest assumption:
- Interviews: Useful for exploring the problem, current workarounds, and decision-making. They can reveal what people say they need, but do not by themselves show that a working product will be adopted.
- Clickable prototype: Useful for testing whether people understand and can navigate a proposed flow. It is not evidence that the underlying service works in real conditions.
- Manual or concierge service: A team can deliver the proposed outcome by hand to learn what users need before automating the process. Account for the extra operational effort and avoid mistaking a highly assisted experience for an automated one.
- Limited functional release: Appropriate when the assumption requires real users to complete a real task, or when behavior such as repeat use or payment is the evidence sought. Keep the release narrow enough to observe and support.
These methods answer different questions; none is automatically suitable for every product. The MVP is the learning mechanism, not a mandatory technology or a particular number of features.
How to cut features without cutting the value
Map the core journey
Write down the shortest sequence of actions that lets the target user receive the proposed value. For each step, ask what must be present for the user to complete it and for the team to learn from the test. Keep features that enable that journey, make the selected experiment possible, or are necessary for essential safety, reliability, privacy, accessibility, or legal obligations.
Use a feature-by-feature cut test
For every proposed feature, ask: “Which user need or hypothesis does this serve, and what evidence would change if we omitted it?” If there is no clear answer, defer it to a later backlog rather than letting it enter the first release by inertia.
Generic registration, business-rules engines, and content-management systems can inflate an MVP when they are not critical to the service; the South Australian Treasury toolkit uses them as examples of scope to question, not functions that should always be removed (South Australian Department of Treasury and Finance, Phase 4: alpha). An account or content system may be essential when the real user journey depends on it.
Build only as much product and infrastructure as the test needs
Do not design for hypothetical scale before the test justifies it, but do not treat quality or obligations as optional. A narrow release still needs appropriate security, accessibility, privacy, and reliability for its users and context. Record trade-offs and known limits so that the team can distinguish an assumption about the product from a failure in the test environment.
Architecture is a context-dependent choice. Microsoft for Startups notes that early decisions affect complexity and later rework, contrasting the relative simplicity of a monolith with the coordination overhead of microservices (Microsoft for Startups MVP guide). Choose an approach your team can build and operate, taking its expertise, expected growth, and operational burden into account; neither architecture is a universal MVP rule.
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Measure evidence, then decide what changes
Choose an observable outcome in advance
Decide what behavior or feedback would support or challenge the hypothesis before users encounter the product. The right measure depends on the question. Examples include:
- Task completion: Can the intended user complete the essential journey?
- Activation: Do users reach the point where they experience the product’s value?
- Retention: Do they return when the problem recurs?
- Conversion: Do they take the relevant next step, such as paying, when payment is the assumption under test?
- Qualitative evidence: What do users struggle with, value, or work around while attempting the task?
Activation, retention, and conversion are examples from Microsoft for Startups, not universal success metrics; the Google News Initiative likewise says measurement depends on the experiment (Microsoft for Startups MVP guide; Google News Initiative Startups Playbook). Set a decision rule appropriate to your test rather than borrowing an unsupported universal threshold.
Choose a next action, not just a score
Before results arrive, decide what you will do if the evidence supports the assumption, contradicts it, or remains unclear: continue with the next focused test, revise the solution or assumption, or stop this line of work. A test that fails to answer the question may need a better experiment, not a larger backlog.
Iteration remains relevant after launch. The Government of Canada’s digital standard describes frequent iteration as a way to respond to changing user needs, standards, and technology across a product’s lifecycle (Government of Canada digital standard).
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- Have we named the intended user and the specific problem?
- Is the riskiest assumption explicit, and does this test directly examine it?
- Can the selected experiment produce useful evidence without a full build?
- Can users experience the core value under the conditions this test requires?
- Does every included feature support the journey, the experiment, or essential operation?
- Have we selected an observable outcome and a decision rule before seeing results?
- Have we recorded operational, privacy, security, accessibility, and other relevant constraints?
The South Australian toolkit attributes its definition of an MVP to Eric Ries’s The Lean Startup (2011, p. 77): “a version of the product that enables a full turn of the Build-Measure-Learn loop with a minimum amount of effort and the least amount of development time.” The useful test is whether the planned experience can complete that learning loop—not whether the roadmap has been compressed into a smaller list.
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