Building a Minimum Viable Product / Learning from data
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This document discusses building a minimum viable product (MVP) using a data-driven approach. It recommends determining key decisions like core use case, differentiation, and go-to-market strategy. The goals of an MVP are product-market fit and scaling. An MVP contains the minimum set of features to learn from early adopters through qualitative and quantitative feedback. It provides examples of metrics to measure like acquisition, activation, engagement, retention, and revenue. The process is to iterate based on learnings from tweaking features and measuring results.
Building a Minimum Viable Product / Learning from data
- 1. Building a Minimum Viable Product a data driven approach Igal Perelman
- 2. MVP The minimum set of features you have to build in order to learn from early adopters (*) Lean Startup - Eric Ries, Steve Blank
- 3. Decisions (assumptions) Culture Core Use Case Differentiation Platform(s) UX Data Go To Market And many others
- 4. Goals Product Market Fit Scale
- 5. MVP The minimum set of features you have to build in order to learn from early adopters
- 6. Learning from early adopters Qualitative (from day 0) Feedback channels Reach out to users Quantitative (from day 1) What should I measure?
- 7. Quantitative New user acquisition New user activation Engagement Retention Revenue (*) User resurrection
- 8. New User Acquisition Invites SEO ASO PPC Social platforms And many other channels...
- 9. ROI Investment / Acquisition channel UI Time $$$ Return / AC == # of new users Activated users Paying users
- 10. New User Activation Signup flow >> Signup page Questions Signup funnel conversion % of new users that complete the core use case What is the core metric?
- 11. Voxer
- 12. Voxer
- 13. Funnel Analysis (*) mixpanel, dummy data.
- 14. Engagement Core use case => engagement unit # of these units Uniques Total
- 15. Pinterest (*) Internet Marketing Inc, Based on ComScore report.
- 16. Retention % of engaged users after X time Cohort analysis Core use case Viability, reliability, speed and usability
- 17. Retention (*) mixpanel, dummy data.
- 18. Iterate Tweak / Remove / Build Measure Iterate based on learnings Metrics User feedback Research
- 19. Create a product that users