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Kontagent Fb Developer Garage Final   Jeff Kontagent Fb Developer Garage Final Jeff Presentation Transcript

  • Metrics for Viral Tuning By: Jeffrey Tseng Kontagent Facebook Developer Garage SF 2009
  • 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
  • SuperPoke Viral Loop Invite Page 1 Invite Page 2 New users notification/invite Viral loop Viral loop 2
  • The Levers A/B Testing Call to Action A/B Testing Message Social Context Product and User Experience Design Application
  • Sender View Social Context Call to Action # of Messages Recipients Sent
  • Sender View Social Context Call to Action # of Messages Recipients Sent Avg msgs Metrics sent/event
  • Recipient View Application Sender Message Acceptance Installs
  • Recipient View Application Sender Message Acceptance Installs Msg % new users Conversion to Metrics Conversion invited Installs
  • 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
  • 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
  • How do you calculate virality?
  • The Viral Co-efficient Day 1 Day 2 Day 3 3 1 1 1 1 1 2 2 Simply the “average branching factor”
  • Viral Rate Co-efficient average branching factor average response time 0.5 1.0 0.5 days days days
  • 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
  • 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
  • What User Demographics Are Most Viral?
  • Answer: It’s very app specific
  • 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
  • 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
  • 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
  • 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
  • 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