Amey-Thakur Product discovery
Discovery de-risks building. The question is never "is this idea good" but "what evidence shows this problem is real, frequent, and painful enough that someone changes behavior to solve it".
Method
- Start from the problem space, held open. Write the target outcome (business and user), then map opportunities beneath it (an opportunity solution tree: outcome, opportunities, candidate solutions, experiments). Solutions arriving first get parked as candidates under whichever opportunity they claim to serve; if none fits, that is the finding.
- Grade evidence by what it cost the user. Weakest to strongest: opinions in a meeting, survey answers, interview stories about the past (see customer-interviews), observed current behavior and workarounds, and paid/effortful commitment (signed up, prepaid, switched tools). Fund decisions on the top half; a deck of survey percentages is not discovery.
- Hunt the workaround. Users solving the problem today with spreadsheets, interns, or duct tape prove frequency and pain at once: the workaround is the demand signal and the competing product. No workaround usually means no felt problem, whatever the interviews politely said.
- Size before you commit. How many users hit this, how often, at what cost each time (see estimation-techniques for honest Fermi work); a real problem afflicting few people rarely is a segment decision, not a roadmap item. Check the result against the outcome it claims to move (see product-metrics).
- Run the cheapest killing experiment. For the riskiest assumption, choose the test that could disprove it fastest: landing-page demand tests, concierge/manual service, fake door (ethically: tell users after; see mvp-scoping), prototype walkthroughs. Write the pass/fail bar before running (see hypothesis-driven-work); experiments without kill criteria are theater.
- Close the loop visibly. Each discovery cycle ends in a decision (pursue, park, kill) recorded with its evidence (see decision-journals, architecture-decision-records' sibling for product); killed ideas with reasons prevent their monthly resurrection.
Boundaries
- Discovery is continuous, not a phase gate; a quarter of pure research with no shipped learning is its own failure mode (see mvp-scoping's ship-to-learn).
- Some bets are vision-driven and evidence-light by nature; label them as such deliberately rather than laundering them through fake validation.
- B2B discovery must separate buyer, user, and blocker personas; one enthusiastic champion is not a validated market (see customer-interviews' recruiting).