Building a Minimum
Viable Product
a data driven approach

Igal Perelman
MVP
The minimum set of features you have to
build in order to learn from early adopters

(*) Lean Startup - Eric Ries, Ste...
Decisions (assumptions)
•
•
•
•
•
•
•
•

Culture
Core Use Case
Differentiation
Platform(s)

UX
Data

Go To Market
And many...
Goals
• Product Market Fit
• Scale
MVP
The minimum set of features you have to build
in order to learn from early adopters
Learning from early adopters
• Qualitative (from day 0)
•
•

Feedback channels
Reach out to users

• Quantitative (from da...
Quantitative
• New user acquisition
• New user activation
•

Engagement

•

Retention

•

Revenue

(*) User resurrection
New User Acquisition
• Invites
• SEO
• ASO
• PPC
• Social platforms
• And many other channels...
ROI
• Investment / Acquisition channel
•
•
•

UI

Time
$$$

• Return / AC == # of new users
•
•

Activated users

Paying u...
New User Activation
• Signup flow >> Signup page
• Questions
•
•
•

Signup funnel conversion

% of new users that complete...
Voxer
Voxer
Funnel Analysis

(*) mixpanel, dummy data.
Engagement
• Core use case => engagement ‘unit’
• # of these units
•
•

Uniques
Total
Pinterest

(*) Internet Marketing Inc, Based on ComScore report.
Retention
• % of engaged users after X time
• Cohort analysis
• Core use case
• Viability, reliability, speed and usabilit...
Retention

(*) mixpanel, dummy data.
Iterate
• Tweak / Remove / Build
• Measure
• Iterate based on learnings
•
•
•

Metrics
User feedback

Research
Create a product that users ♥
Q&A

more questions?
@iperelman
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Building a Minimum Viable Product / Learning from data

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Slides from my talk last week @ Berkeley, Haas School of Business.

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Building a Minimum Viable Product / Learning from data

  1. 1. Building a Minimum Viable Product a data driven approach Igal Perelman
  2. 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. 3. Decisions (assumptions) • • • • • • • • Culture Core Use Case Differentiation Platform(s) UX Data Go To Market And many others…
  4. 4. Goals • Product Market Fit • Scale
  5. 5. MVP The minimum set of features you have to build in order to learn from early adopters
  6. 6. Learning from early adopters • Qualitative (from day 0) • • Feedback channels Reach out to users • Quantitative (from day 1) • What should I measure?
  7. 7. Quantitative • New user acquisition • New user activation • Engagement • Retention • Revenue (*) User resurrection
  8. 8. New User Acquisition • Invites • SEO • ASO • PPC • Social platforms • And many other channels...
  9. 9. ROI • Investment / Acquisition channel • • • UI Time $$$ • Return / AC == # of new users • • Activated users Paying users
  10. 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. 11. Voxer
  12. 12. Voxer
  13. 13. Funnel Analysis (*) mixpanel, dummy data.
  14. 14. Engagement • Core use case => engagement ‘unit’ • # of these units • • Uniques Total
  15. 15. Pinterest (*) Internet Marketing Inc, Based on ComScore report.
  16. 16. Retention • % of engaged users after X time • Cohort analysis • Core use case • Viability, reliability, speed and usability
  17. 17. Retention (*) mixpanel, dummy data.
  18. 18. Iterate • Tweak / Remove / Build • Measure • Iterate based on learnings • • • Metrics User feedback Research
  19. 19. Create a product that users ♥
  20. 20. Q&A more questions? @iperelman

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