Kontagent Fb Developer Garage Final Jeff

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Kontagent Fb Developer Garage Final Jeff

  1. 1. Metrics for Viral Tuning By: Jeffrey Tseng Kontagent Facebook Developer Garage SF 2009
  2. 2. PHAME Growing the application efficiently Problem Virality can be engineered Hypothesis Testing variants, iterating quickly Action Viral factor, conversions Metrics Different contexts, call to actions, messaging Experiments
  3. 3. SuperPoke Viral Loop Invite Page 1 Invite Page 2 New users notification/invite Viral loop Viral loop 2
  4. 4. The Levers A/B Testing Call to Action A/B Testing Message Social Context Product and User Experience Design Application
  5. 5. Sender View Social Context Call to Action # of Messages Recipients Sent
  6. 6. Sender View Social Context Call to Action # of Messages Recipients Sent Avg msgs Metrics sent/event
  7. 7. Recipient View Application Sender Message Acceptance Installs
  8. 8. Recipient View Application Sender Message Acceptance Installs Msg % new users Conversion to Metrics Conversion invited Installs
  9. 9. Application View A/B Test # of Messages Social Avg msgs Call to Action Sent Context sent/event Sender Application Recipient Msg Acceptance Message Conversion A/B Test % new users Conversion to Installs invited Installs
  10. 10. The Metrics Avg msgs • Average message/event sent/event • Msg conversion rate Msg Conversion • % of New Users Invited % new users • Conversion to Installs invited Conversion to Installs • Repeat visits to the event! Repeat Visits to the Event
  11. 11. How do you calculate virality?
  12. 12. The Viral Co-efficient Day 1 Day 2 Day 3 3 1 1 1 1 1 2 2 Simply the “average branching factor”
  13. 13. Viral Rate Co-efficient average branching factor average response time 0.5 1.0 0.5 days days days
  14. 14. Viral Rate vs. Viral Co-efficient • Viral co-efficient – Has no time dimension – Can be used to tell if an app is viral – Can be used for viral tuning (trending) – Cannot be used to compare growth rate • Viral rate – How fast does the app grow? – Can be used to compare growth rate of diff apps – Can be used for projections
  15. 15. Why Track the Viral Tree? • Absolute long-term measure of effectiveness • Lifetime Network Value • Tracking sources of installs (attribution) – Paid source vs. organic • Identify the most socially active users – Message or treat them differently
  16. 16. What User Demographics Are Most Viral?
  17. 17. Answer: It’s very app specific
  18. 18. Application 1 Application 2 24% 33% Female Female 42% Gender Male Male 56% 20% Unknown Unknown 25% 6.2 7.6 9.6 8.8 Invites Sent/User 6.7 7.2 40.03% 39.40% 12.17% 12.66% 32.13% 10.46% Acceptance Rate Female Male Unknown Female Male Unknown
  19. 19. Application 2 Application 1 7% 15% 0-17 (School) 0-17 (School) 24% 40% 47% 14% Age 18-24 (College) 18-24 (College) 25+ (Work) 25+ (Work) Distribution Unknown Unknown 24% 29% 7.8 7.8 5.4 8.8 0-17 0-17 (School) Invites 18-24 18-24 (College) 6.3 Sent/User 25+ 25+ (Work) Unknown 5.6 Unknown 10.7 10.1
  20. 20. Demographics for Viral Tuning • Demographics – Distribution does not equal behavioral distribution – Measure the behavior of the demographic/segment • User segmentation is useful – You can’t test EVERYTHING – Segment users allows you to focus your testing
  21. 21. Final Notes on Viral Metrics • Viral Metrics CAN – Provide framework for test and experiment – Allow you to iterate quickly – A/B testing for small changes Viral Metrics CANNOT build you a good product The hard work is being creative
  22. 22. Long Term vs. Short Term • Short term – A/B for copy is a short-term local metrics • Optimize for long-term metrics – Time on site, – LTV – LNV – ARPU

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