Lean MVP Validation: A Practical 7-Step Startup Checklist
Step 1: Define success metrics that matter
Start by naming a primary metric that reflects the core learning goal (for example, activation rate or signups).Set 2-3 secondary metrics to monitor alongside.Make metrics SMART: specific, measurable, achievable, relevant, time-bound.Example: aim for a 5% signup-to-activation rate within 30 days; retain 25% of daily active users after 14 days.Tip: ensure metrics tie back to the problem you are solving; avoid vanity metrics like total signups alone.Step 2: Validate the problem before solution
Conduct 8-12 in-depth interviews with potential users to uncover real pains.Use open-ended questions that explore the user journey and pain points without leading answers.Document pains, frequency, and impact; look for common threads across interviews.Decide if the problem is widespread enough to justify a solution.Step 3: Validate the solution with rapid prototypes
Translate top pains into a simple hypothesis: if we provide X, then Y happens.Create lo-fi prototypes or sketches to test the concept quickly.Use a landing page or explainer video to gauge interest and collect emails or waitlists.Gather qualitative feedback and refine the hypothesis.Step 4: Prioritize features for the MVP
Use a simple framework like MoSCoW or RICE to separate must-haves from nice-to-haves.Map core user flows and identify the handful of screens that allow you to learn the most.Limit the MVP to 3-5 features that validate the learning goals.Document what success looks like for each feature (for example, a user completes a task in under 2 minutes).Step 5: Plan the lean MVP build
Write a concise spec: the MVP should support learning, not every edge case.Break work into small, testable tasks; aim for a 2-6 week build window.Decide on the data you will collect from day one (events, funnels, retention).Plan for quick iterations after you gather the first feedback.Step 6: Run cheap experiments before heavy development
Build landing pages or waitlists to test demand for the solution.Run a small ad or social campaign and measure click-to-signup actions.Use fake doors or smoke tests to validate the interest without building full functionality.Learn from the numbers: a strong early signal is a clustering of positive responses above a predefined threshold.Step 7: Measure, learn, and iterate
Establish a weekly learning loop: review analytics, interview notes, and user feedback.Update a prioritized backlog focused on learning outcomes.Decide whether to persevere, pivot, or pause based on data and the learning pace.Use cohort analysis to observe how changes affect activation, retention, and referral.These steps help you de-risk product-market fit by turning assumptions into data, cheap experiments into actionable learning, and a clear path to the next build.
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