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

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

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Learning to be a lean startup Presentation Transcript

  • 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
  • 3. Our usersLarge enterprises act as seeding points to thousand of suppliers
  • 4. Me: @anickelsenBuild/release pipeline Data warehouse Lean startup
  • 5. Our path to lean startup
  • 6. 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)
  • 7. Lean Startup: The solution?• The principles – Build  measure  learn1. Find the core assumptions (in hypothesis)2. Test them using the least amount of effort3. Measure the impact4. Conclude on idea validity5. Iterate=> “validated learning”
  • 8. Lean startup: Year One• Start small• Start with new projects• Start isolated in existing projects• Use good, automatic tools• Make it visible• Involve everyone
  • 9. Start small
  • 10. Instant payments
  • 11. We called „the users‟that had „signed up‟
  • 12. We installed „the app‟
  • 13. Documents were accepted
  • 14. Money transferred „automatically‟
  • 15. The invoice was paid
  • 16. We built a product
  • 17. Start with something new
  • 18. Reduces user expectations, old code and old data
  • 19. CloudScan
  • 20. Users signed up and saw „the product‟
  • 21. Documents were „scanned‟
  • 22. „automatically‟
  • 23. We iterate onthe product today
  • 24. Isolate experiments
  • 25. Isolate to learn to:Formulate theories expect outcome,measure behavior, analyze data, conclude
  • 26. Death by data
  • 27. The Zombie Apocalypse
  • 28. Improve k-factor by improving conversion rate
  • 29. 50%increase
  • 30. Our tools
  • 31. Tooling• Feature toggles• Own experimentation framework• ABinator – own experiment analysis tool• Improved dashboards
  • 32. ABinator
  • 33. Visibility
  • 34. “What did that?”
  • 35. “What did that?”“Why did it do that?”
  • 36. “What did that?”“Why did it do that?” “Is it good?”
  • 37. Visibility improves data integrity
  • 38. Visibility improvesproduct understanding
  • 39. Involve everybody
  • 40. Present running experiments
  • 41. Enableself-service analysis
  • 42. Present findings
  • 43. Looking back
  • 44. Are we there yet?
  • 45. Small populations High variationLong-running experiments
  • 46. Is it enough?
  • 47. We think lean now
  • 48. Experimentationframework helps us
  • 49. Will we get there?
  • 50. What have we learned?• Start small• Start with new projects• Start isolated in existing projects• Use good, automatic tools• Make it visible• Involve everyone
  • 51. Learning to be a lean startup ani@tradeshift.com