This document discusses the importance of data driven development. It emphasizes using metrics and hypothesis testing to iteratively build and validate minimum viable products (MVPs). Key aspects covered include defining metrics, developing hypothesis statements, building MVPs, monitoring metrics, learning from data, and continuous improvement through addressing technical debt. The overall message is that data should guide decision making throughout the development process.

1. Data Driven Development
Tyler Henkel
@tyhenkel
github.com/DaftMonk

2. Ideas are cheap

3. Successful Companies Pivot

4. Why Data Driven Development
Matters

6. The problem with typical development
Waterfall Agile

7. The Feedback Loop
PIVOT /
PERSEVERE
HYPOTHESIS TEST /
GATHER DATA

8. Test Driven Development
REFACTOR
A Lean Startup is a test-driven company
RED
GREEN ITERATE
TEST
VALIDATE

9. Actionable Metrics

11. Define Key Metrics
Category User Status Conversion
Acquisition Visited website 100%
Activation Signed-up 20%
Activation Downloaded 10%
Retention Did Key Activity 5%
Retention Re-Visited 4%
Revenue Purchased 2%
Referral Review / Rated 1%

12. Develop a Hypothesis Statement
1. Problem
2. Customer segment
3. Solution
4. Success metric
Hypothesis consists of:
Hypothesis MVP Iterate

13. Hypothesis Validation
Should we even work on
this?

14. Hypothesis Validation
Analyzing the data
User feedback

15. What Will Success Look Like?
Phase 1 Phase 2 Phase 3
Collect
Memes
? Profit

16. What Will Failure Look Like?

17. What Will Failure Look Like?
Old
Sign Up Link Login Form
New
Sign up Form
Login Link

18. “It’s not broken” Validation

19. Building the
Minimum Viable
Product
Hypothesis MVP Iterate

20. Why an MVP?

21. [An MVP] is a concise summary of the smallest
possible group of features that will work as a
stand-alone product while still solving at least the “core”
problem and demonstrating the product’s value.
“
- Steve Blank

22. The Skateboard MVP

23. MVP Examples

24. MVP Examples

25. MVP Examples

26. Technical Considerations
Don’t Focus on Scalable
Make Eliminating MVPs Easy
Card Technical Debt

27. Ways to Validate an MVP
Split (A/B) Test
Customer Feedback
Monitoring Before and After
MVP IterateHypothesis

28. Monitoring the MVP

29. Learning from the MVP

30. How Can I Implement This?

31. Visual Website Optimizer

38. Track in More Than One Way

39. Heap Analytics

40. Is a feature being used?
Started
tracking
here

41. Who is using a feature?

42. Fixing Usability Issues

43. Fixing Usability Issues

44. Funnel Funnel By Cohort
6.22% Conversion 7.32% Conversion

45. Segments in Heap

46. Fullstory

49. Continuous Deployment
Release early, Release often
Smoke Tests
Utilize dark launches

50. Handling MVP debt

51. Types of Technical Debt
Reckless Prudent
Deliberate
Inadvertent
“We don’t have time
for design”
“We must ship now
and deal with
consequences”
“We now know how we
should have done it”
“What is Layering?”

52. UX Debt

53. Best Practices For MVP Debt
Plan your debt
Prioritize debt
Pay off your debt

54. Build a Data Culture

55. Questions?

56. Thank You
joind.in/talk/a8a24
Tyler Henkel
@tyhenkel