David Binetti Lean Startup Votizen Pivot at SLLCONF 2011
•
30 likes•15,794 views
The document discusses when and how to pivot a startup business model based on lessons learned from iterating on various versions of a civic engagement platform. It provides examples of pivoting the product, problem, market, and metrics based on testing results to find more sustainable business models and higher conversion rates. The key lessons are that pivots involve restating assumptions based on learnings rather than just changing features, and getting customer feedback is crucial for uncovering new opportunities.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to David Binetti Lean Startup Votizen Pivot at SLLCONF 2011
Similar to David Binetti Lean Startup Votizen Pivot at SLLCONF 2011 (20)
More from Eric Ries
More from Eric Ries (20)
Recently uploaded
Recently uploaded (20)
David Binetti Lean Startup Votizen Pivot at SLLCONF 2011
- 1. When and How to Pivot Presented at Startup Lessons Learned 23 May 2011 David Binetti @dbinetti
- 2. Preface • Share! • No Original Thoughts... • We’re Hiring! slideshare.com/votizen @dbinetti
- 3. A Bit About Me • Business Model Designer at Votizen • Mentor at Hacker Dojo • Six Startups both Agile and Fixed @dbinetti
- 4. A Bit About You • Startups? • Something else? • Unsure? @dbinetti
- 5. High Finance @dbinetti
- 6. High Finance 33% 33% 33% @dbinetti
- 7. High Finance 10% 30% 30% 30% @dbinetti
- 8. High Finance 13% 20% 20% 20% 7% 20% @dbinetti
- 9. High Finance 12% 20% 12% 20% 12% 5% 19% @dbinetti
- 10. High Finance Sells for: $50M 12% 20% 12% Your stake: ~$6M 20% 12% Chance of Success: 10% 5% 19% Expected Value: $600K @dbinetti
- 11. High Finance Startup Google Y1 0 175 Y2 40 175 Y3 120 200 Y4 120 200 Y5 120 + 600 250 @dbinetti
- 12. High Finance Startup Google Y1 0 175 Y2 40 175 Y3 120 200 Y4 120 200 Y5 120 + 600 250 @dbinetti
- 13. High Finance Startup Google Y1 0 175 Y2 40 175 Y3 120 200 Y4 120 200 Y5 120 + 600 250 Expected 1.0 M 1.0 M @dbinetti
- 14. High Finance Startup Google Y1 0 175 Y2 40 175 Y3 120 200 Y4 120 200 Y5 120 + 600 250 Expected 1.0 M 1.0 M Variance 3.6 x 10^12 0 @dbinetti
- 15. High Finance • You must be high if you think this makes financial sense. • But money isn’t everything. • What makes the entrepreneur different? @dbinetti
- 16. @dbinetti
- 17. @dbinetti
- 18. @dbinetti
- 19. @dbinetti
- 20. Why Lean? • Mitigates your reality distortion field. • Helps avoid (expensive) false positives. • Converts market risk to technical risk @dbinetti
- 21. Lean Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 22. Lean Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 23. Lean Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 24. Lean Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 25. On Pivots @dbinetti
- 26. On Pivots @dbinetti
- 27. On Pivots • Pivots are restatements of your business model; not synonymous with change. • Pivots are a consequence of learning about your business, not just your product. • Pivots are vision-driven, not testing-driven. @dbinetti
- 28. Our Vision • Social media is disrupting Politics. • Voters can now join with each other and connect to their officials directly. • Facilitating meaningful connections will result in a new form of civic participation that will profoundly change our world. @dbinetti
- 29. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 30. 1.0 1.1 2.0 3.0 4.0 Model Hypothesis Product Facebook for politics Problem People want a stronger voice Channel Social networks Demand Viral (self-perpetuating demand) Market Resegmented (donations engine) Competitive Hyper-fragmented @dbinetti
- 31. 1.0 1.1 2.0 3.0 4.0 Metric Description Acquisition Creates account Activation Certified authenticity Referrals Forwards to friends Retention Uses system thrice or greater Revenue Supports causes @dbinetti
- 32. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 33. 1.0 1.1 2.0 3.0 4.0 • Did: The “MVP”, six weeks at $1206 • Learned: It’s a start. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% Activation 17% Referrals - Retention - Revenue - @dbinetti
- 34. 1.0 1.1 2.0 3.0 4.0 • Did: The “MVP”, six weeks at $1206 • Learned: It’s a start. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% Activation 17% Referrals - Retention - Revenue - @dbinetti
- 35. 1.0 1.1 2.0 3.0 4.0 • Did: The “MVP”, six weeks at $1206 • Learned: It’s a start. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% Activation 17% Referrals - Retention - Revenue - @dbinetti
- 36. 1.0 1.1 2.0 3.0 4.0 • Did: A/B testing to optimize • Learned: Significant jump in conversion. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% Activation 17% 90% Referrals - 4% Retention - 5% Revenue - - @dbinetti
- 37. 1.0 1.1 2.0 3.0 4.0 • Did: A/B testing to optimize • Learned: Significant jump in conversion. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% Activation 17% 90% Referrals - 4% Retention - 5% Revenue - - @dbinetti
- 38. 1.0 1.1 2.0 3.0 4.0 • Did: A/B testing to optimize • Learned: Significant jump in conversion. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% Activation 17% 90% Referrals - 4% Retention - 5% Revenue - - @dbinetti
- 39. 1.0 1.1 2.0 3.0 4.0 • Did: A/B testing to optimize • Learned: Significant jump in conversion. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% Activation 17% 90% Referrals - 4% Retention - 5% Revenue - - @dbinetti
- 40. 1.0 1.1 2.0 3.0 4.0 • Did: Same process as before • Learned: Missing something important. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% Activation 17% 90% Referrals - 4% Retention - 5% Revenue - - @dbinetti
- 41. 1.0 1.1 2.0 3.0 4.0 • Did: Same process as before • Learned: Missing something important. Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% Activation 17% 90% Referrals - 4% Retention - 5% Revenue - - @dbinetti
- 42. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 43. When to Pivot • Surveys, A/B testing, landing pages, etc. are all good ways to find a local maximum for a given set of assumptions. • If you’ve hit that local max and your model isn’t a sustainable business, it’s time to restate your assumptions (ie, to pivot.) @dbinetti
- 44. How to Pivot • Put down the dry erase and step away from the whiteboard. • You earned what you learned; don’t flail. • Vision + Learning = Inspiration @dbinetti
- 45. In My Case • Looked past the product to the nature of the problem. • “I’ve always wanted to get involved. This makes it so much easier.” • Put myself in the “let’s make it easier” mindset and got out of the building. • Lead to the inspiration for @2gov. @dbinetti
- 46. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 47. 1.0 1.1 2.0 3.0 4.0 Model Hypothesis Product Simple messaging tool Problem Getting involved is hard Channel Social networks Demand Viral (self-perpetuating demand) Market Resegmented (donations engine) Competitive Hyper-fragmented @dbinetti
- 48. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 49. 1.0 1.1 2.0 3.0 4.0 • Did: Asymmetric message delivery • Learned: Found a new local max on revenue Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% Activation 17% 90% 83% Referrals - 4% 54% Retention - 5% 21% Revenue - - 1% @dbinetti
- 50. 1.0 1.1 2.0 3.0 4.0 • Did: Asymmetric message delivery • Learned: Found a new local max on revenue Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% Activation 17% 90% 83% Referrals - 4% 54% Retention - 5% 21% Revenue - - 1% @dbinetti
- 51. 1.0 1.1 2.0 3.0 4.0 • Did: Asymmetric message delivery • Learned: Found a new local max on revenue Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% Activation 17% 90% 83% Referrals - 4% 54% Retention - 5% 21% Revenue - - 1% @dbinetti
- 52. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 53. 1.0 1.1 2.0 3.0 4.0 Model Hypothesis Product Simple involvement tool Problem People want to be good citizens Channel Social networks Demand Viral (self-perpetuating demand) Market Resegmented (campaign management) Competitive Hyper-fragmented @dbinetti
- 54. Focus on Revenue • Website, analytics, dashboards. • Custom work specific to your issue. • People loved it! Let’s Build! @dbinetti
- 55. 1.0 1.1 2.0 3.0 4.0 • Did: New tools and analytics • Learned: No signature means no sale Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% 43% Activation 17% 90% 83% 85% Referrals - 4% 54% 52% Retention - 5% 21% 24% Revenue - - 1% 0% @dbinetti
- 56. 1.0 1.1 2.0 3.0 4.0 • Did: New tools and analytics • Learned: No signature means no sale Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% 43% Activation 17% 90% 83% 85% Referrals - 4% 54% 52% Retention - 5% 21% 24% Revenue - - 1% 0% @dbinetti
- 57. 1.0 1.1 2.0 3.0 4.0 • Did: New tools and analytics • Learned: No signature means no sale Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% 43% Activation 17% 90% 83% 85% Referrals - 4% 54% 52% ! Retention - 5% 21% 24% Revenue - - 1% 0% @dbinetti
- 58. 1.0 1.1 2.0 3.0 4.0 • Did: New tools and analytics • Learned: No signature means no sale Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% 43% Activation 17% 90% 83% 85% Referrals - 4% 54% 52% ! Retention - 5% 21% 24% Revenue - - 1% 0% @dbinetti
- 59. Don’t Underestimate • Easy to fool yourself • “Show Yourself the Money” • This is why PR and vanity metrics are so dangerous. @dbinetti
- 60. The Framework Formulate Iterate, Release & Model Measure Pivot @dbinetti
- 61. Learning • Poor Sales Cycle (contract, cost, channel) • “I’m just not sure about this Twitter thing.” • Make the engagement easy to try and show ROI out of the gate. @dbinetti
- 62. 1.0 1.1 2.0 3.0 4.0 Model Hypothesis Product Simple involvement tool Problem People want to be good citizens Channel Social networks Demand Viral (self-perpetuating demand) Market Resegmented (look like AdWords) Competitive Hyper-fragmented @dbinetti
- 63. Ad Words for Activism • Special Delivery on a per-message basis • Only pay for value • Initial sale: $200 first day. @dbinetti
- 64. 1.0 1.1 2.0 3.0 4.0 • Total investment at this point: ~$120K and 18 months. • Model has promise; raise some funds Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% 43% 51% Activation 17% 90% 83% 85% 92% Referrals - 4% 54% 52% 64% Retention - 5% 21% 24% 28% Revenue - - 1% 0% 11% @dbinetti
- 65. 1.0 1.1 2.0 3.0 4.0 • Total investment at this point: ~$120K and 18 months. • Model has promise; raise some funds Metric v.1 v.1.1 v.2 v.3 v.4 Acquisition 5% 17% 42% 43% 51% Activation 17% 90% 83% 85% 92% Referrals - 4% 54% 52% 64% Retention - 5% 21% 24% 28% Revenue - - 1% 0% 11% @dbinetti
- 66. Important Notes • Iteration loops got tighter. • Each validation justified a higher level of investment/engagement. • 1K - 10K - 100K - 1000K • Seek funding when you think you have more technical risk than market risk. @dbinetti
- 67. Summary • Set Model so you know what your measuring and when you’re done. • Use Metrics to find local maxima, not to find a business. • Pivots are vision-driven. @dbinetti
- 68. Feedback • bit.ly/sllconfdb • slideshare.net/votizen • @votizen @dbinetti
- 69. Final Thought @dbinetti
- 70. @dbinetti
Editor's Notes
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n