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.

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Learning to be a lean startup

  1. 1. Learning to be a lean startup Anders Nickelsen Tradeshift Oct 2012
  2. 2. Tradeshift• A platform for all your business interactions• Core feature is electronic invoicing• Free to use
  3. 3. Our usersLarge enterprises act as seeding points to thousand of suppliers
  4. 4. Me: @anickelsenBuild/release pipeline Data warehouse Lean startup
  5. 5. Our path to lean startup
  6. 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. 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. 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. 9. Start small
  10. 10. Instant payments
  11. 11. We called „the users‟that had „signed up‟
  12. 12. We installed „the app‟
  13. 13. Documents were accepted
  14. 14. Money transferred „automatically‟
  15. 15. The invoice was paid
  16. 16. We built a product
  17. 17. Start with something new
  18. 18. Reduces user expectations, old code and old data
  19. 19. CloudScan
  20. 20. Users signed up and saw „the product‟
  21. 21. Documents were „scanned‟
  22. 22. „automatically‟
  23. 23. We iterate onthe product today
  24. 24. Isolate experiments
  25. 25. Isolate to learn to:Formulate theories expect outcome,measure behavior, analyze data, conclude
  26. 26. Death by data
  27. 27. The Zombie Apocalypse
  28. 28. Improve k-factor by improving conversion rate
  29. 29. 50%increase
  30. 30. Our tools
  31. 31. Tooling• Feature toggles• Own experimentation framework• ABinator – own experiment analysis tool• Improved dashboards
  32. 32. ABinator
  33. 33. Visibility
  34. 34. “What did that?”
  35. 35. “What did that?”“Why did it do that?”
  36. 36. “What did that?”“Why did it do that?” “Is it good?”
  37. 37. Visibility improves data integrity
  38. 38. Visibility improvesproduct understanding
  39. 39. Involve everybody
  40. 40. Present running experiments
  41. 41. Enableself-service analysis
  42. 42. Present findings
  43. 43. Looking back
  44. 44. Are we there yet?
  45. 45. Small populations High variationLong-running experiments
  46. 46. Is it enough?
  47. 47. We think lean now
  48. 48. Experimentationframework helps us
  49. 49. Will we get there?
  50. 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. 51. Learning to be a lean startup ani@tradeshift.com

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