At Tradeshift, we've followed the lean start-up principles for a year. In this presentation, I present the lessons learned.

1. Learning to be a lean startup
Anders Nickelsen
Tradeshift
Oct 2012

2. Tradeshift
• A platform for all your business
interactions
• Core feature is electronic invoicing
• Free to use

4. Our users
Large enterprises act as
seeding points to
thousand of suppliers

5. Me: @anickelsen
Build/release pipeline
Data warehouse
Lean startup

6. Our path to lean startup

7. The motivation
• Tradeshift is flexible
– 3 month cycles, team re-shuffle and re-focus
• Sprints, user tests, iterations, dashboards
– one way street => ship it and pray
– 10-20 releases per month
• K-factor, cohort analysis (over time)

8. Lean Startup: The solution?
• The principles
– Build  measure  learn
1. Find the core assumptions (in hypothesis)
2. Test them using the least amount of effort
3. Measure the impact
4. Conclude on idea validity
5. Iterate
=> “validated learning”

9. Lean startup: Year One
• Start small
• Start with new projects
• Start isolated in existing projects
• Use good, automatic tools
• Make it visible
• Involve everyone

10. Start small

11. Instant payments

14. We called „the users‟
that had „signed up‟

15. We installed „the app‟

16. Documents were accepted

17. Money transferred
„automatically‟

18. The invoice was paid

19. We built a product

20. Start with something new

21. Reduces user expectations,
old code and old data

22. CloudScan

24. Users signed up and
saw „the product‟

25. Documents were
„scanned‟

26. „automatically‟

27. We iterate on
the product today

28. Isolate experiments

29. Isolate to learn to:
Formulate theories
expect outcome,
measure behavior,
analyze data,
conclude

30. Death by data

31. The Zombie Apocalypse

32. Improve k-factor
by improving
conversion rate

35. 50%
increase

36. Our tools

37. Tooling
• Feature toggles
• Own experimentation framework
• ABinator – own experiment analysis tool
• Improved dashboards

38. ABinator

39. Visibility

40. “What did that?”

41. “What did that?”
“Why did it do that?”

42. “What did that?”
“Why did it do that?”
“Is it good?”

43. Visibility improves
data integrity

44. Visibility improves
product understanding

45. Involve everybody

46. Present running
experiments

47. Enable
self-service analysis

48. Present findings

49. Looking back

50. Are we there yet?

51. Small populations
High variation
Long-running experiments

52. Is it enough?

53. We think lean now

54. Experimentation
framework helps us

55. Will we get there?

56. What have we learned?
• Start small
• Start with new projects
• Start isolated in existing projects
• Use good, automatic tools
• Make it visible
• Involve everyone

57. Learning to be a lean startup
ani@tradeshift.com