Problem-First MVP Strategies
Startup Product Strategy Corrected & verified

Problem-First MVP Strategies

Published by When Notes Fly · View original ↗

Problem-first MVP: deeply understand pain point before building solution, validate problem exists, test willingness to pay for solution.

What has been corrected on this page?

Every accepted correction to this page is recorded with the exact change, so readers can see how the page improved over time.

  1. 29 July 2026 · corrected by Emir Baycan

    Corrected 4 unverified or altered citations: an entirely unverified 'Dirk Bohmer and Michael Lewrick, Research-Technology Management 2018' study on Swiss startup founders (no such paper or co-author exists); unverified statistics ('80 companies', '3.2 times', '7.3 years') added to Rita McGrath's real book Seeing Around Corners; an unverified 'Harvard research team, 100 failed product launches, 2012-2015' study with unverified 84%/62%/8% figures attached to Clayton Christensen's real Jobs-to-Be-Done framework (his real figure is a 75-85% general failure-rate range, not this specific breakdown); and Tony Fadell's real 89% programmable-thermostat statistic misattributed as his own personal research finding when it actually originates from ACEEE (American Council for an Energy-Efficient Economy) data he cited.

    What the page claimed

    Article cited specific researchers, institutions, journals, and statistics for design-thinking and product-failure research that, on independent verification, were either entirely unverified or real frameworks/figures dressed up with unverified precision and false attribution.

    What was corrected

    Removed the unverified Bohmer/Lewrick study details while preserving Lewrick's real, general published findings; stripped unverified statistics from the real McGrath citation; corrected the Christensen citation to his real, verifiable Jobs-to-Be-Done distinction rather than the included an unverified study; corrected the Fadell/Nest thermostat statistic's attribution to its real source (ACEEE data) rather than personal research.

    Why: Real experts, books, and statistics were used as anchors for unverified specifics (unverified co-authors, unverified studies, unverified percentages) or misattributed real data, the same fabrication pattern found across this batch of legacy articles.

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