Alan Avidan


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Alan Avidan

  1. 1. Welcome to:Real-Time Optimization:Putting Facebook User Attributes to Work - Going Beyond A/B Testing and User Segmentation - Optimizing Open GraphAlan Avidan − Executive
  2. 2. We’ll Cover:1. The Playground: Games/Apps/Campaigns2. Which User Attributes Can You Use for Optimization?3. Predictive Best-Fit Optimization, and How Does it Lift KPIs like Revenue, Virality, Engagement, Retention4. Traditional Optimization Tools: Analytics, A/B Testing, User Segmentation5. Open Graph Optimization with Predictive Best-Fit
  3. 3. • Lots of Successful Apps, Games and Campaigns with Millions of (Individual) Users• Low Retention, Low %Pay, High User Acquisition Costs• Notifications/Posts Can Become Spammy and Blocked• KPIs Under Pressure – Need Lift - Perform or Perish!• Vast Amounts of User Attributes
  4. 4. Terminology • Attributes • Elements (Events/Decision Points) • Options (Variants) Low Range High Range
  5. 5. User DNA - Attributes Sources Facebook attributes: Friends, Influence, Likes, Interests, Posts, Events, etc. Open Graph: scores, achievements, published stories, custom actions, etc. Behavioral attributes: level, spending, score, health, custom, etc. Session attributes: time of day, day, duration, etc. Geo-Demographic attributes: age, gender, education, country, etc. 3rd Party attributes: income level, education, etc.
  6. 6. Predictive Best-Fit – Core ConceptsPredictive Best-Fit Algorithms Find Correlations Between User DNA and Conversions User Social, open-graph Predictive Best-Fit DNA Generation Algorithm Real- User and Behavioral Data Time action
  7. 7. Traditional Optimization Technologies A Quick Tour Analytics Segmentation A/B Testing
  8. 8. A/B TestingDefine options Split traffic Measure results Deploy winner Max Result high range Low range High range
  9. 9. A/B Testing – Bottom Line Upside • Conceptually simple and understandable Can achieve good results – up to a point Downside: • One-size-fits-all • Results may deteriorate over time
  10. 10. A Priori Segmentation Define segments Define Options and rule Result base Low range high range
  11. 11. A Priori Segmentation Upside • Can be effective if segmentation was meaningful Downside • Segments are predefined and cannot be changed during the analysis • Different elements might require different segments • Hard to scale in terms of data-set and number of elements • Hard to fine-tune
  12. 12. Clustering Segmentation Define A/B test Segment users Deploy winner options options based on resultLowrangeHigh range
  13. 13. Clustering Segmentation Upside: • Highest Lift • Discover correlations you never knew existed Downside: • Requires storage of terabytes of data • Need really smart people to work on it • Effort = Very High
  14. 14. Predictive Best-Fit• Can optimize in-app and open graph performance• Automated end-to-end solution (Acquire data, analyze, predict, enact)• Machine self-learning• Real-time• No user history required• Numerous data sources• In full compliance with facebook privacy rules• Deep new insights Effort/Resources
  15. 15. Elements For PredictiveBest-Fit Optimization Open Graph Engagement Retention • Publish Yes/No? • Offers • Email • Timing • Products • Message Timing • Art and Copy • Content • Incentives • Call-to-Action • Communications • Gifts • Story Virality Look & Feel Monetization • Share Messages • Colors • Payment Page: • Invite Friends • Graphics Ranges, Incentives • Layouts • Shop Order
  16. 16. Open Graph Big Impact SongPop Hits Major Milestones Just Three Months After Launch • 25 Million unique players to date • Has consistently received a coveted 5 start rating • 4 million people play every day, and growing Ford created an app that publish a story each time a user customized his dream Mustang and then battle others’ model. Although their goal was 2 million engagement they had more than 5 millions and more than 17,000 referrals. Since revamping Open Graph stories with custom art and content, BINGO Blitz got 20% more likes and comments on news feed stories and 500% more unique clicks to the game. The food finding and sharing app has seen a 3X increase in number of visits and activities shared by helping people share the dishes they want, try and ate with friends on Facebook
  17. 17. Open Graph Optimizations 1 2 3 4 5 6 1 2 3 4 Publish by User – Yes/No Story Image Landing Page 5 6 Action Verb Object Time
  18. 18. Open Graph 1 Publish by User – Yes/No Yes No Publish only by the right users!
  19. 19. Open Graph 2 StoryPost with the right content to engage the viewer• Publish achievements the player unlocked• Publish scores the player achieved• Publish custom activities: Jeff E. finished Level 4 on MyGame!• Publish extended custom activities: Jeff E. won a game against Chris on MyGame!
  20. 20. Open Graph 3 Image Option A Option A Image of song, leading to Image taken from to clip game Option B Option B Image of genre, leading Image of real-world friends to songs/albums landscape recently listened to by user Publish using the most effective creative
  21. 21. Open Graph 4 Landing Page Option A Landing page with the song playing Option B Landing page with the latest songs of that genre listened by friends’ Option C Landing page of that album with a discount coupon Publish with the best landing page to convert the viewer
  22. 22. Open Graph 5 Action Verb Object listen Option A Justin listened to [SONG X] by [SINGER-NAME] on Spotify Option B Justin listened to Classic [GENRE Y] music on Spotify Publish the most effective actions and objects
  23. 23. Open Graph 6 Timing Publish at the right time to get maximal exposure Friends newsfeeds
  24. 24. The Last WordConsider optimization if you wish tobecome successful or stay relevantConsider Predictive Best-Fit OptimizationAll the Gain without the Pain
  25. 25. Welcome to: Real-Time Optimization: Putting Facebook User Attributes to Work - Going Beyond A/B Testing and User Segmentation - Optimizing Open GraphAlan Avidan − Executive Director, Business