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Predicting Player Behaviors:Lessons from TelCo’s & FinanceNick LimCEO, Sonamine
Agenda1.   Life cycle management2.   Case study: conversion3.   What are predictives?4.   Case study: AT&T5.   Predictives...
Chapter 1Life Cycle Management
Life Cycle Management• Communication tailored to customer’s stage:  1) Welcome & educate. (“Here’s how”)  2) Upsell. Seek ...
Best Practices from TelcosThese companies learned:• More-engaged customer     Easier to upsell• More upselling   Lower chu...
Chapter 2      Case study:Social game conversion     © 2009-11 Sonamine LLC.
Case Study: Korean Social Game     © 2009-11 Sonamine LLC.
Case Study: Korean Social GameOpportunity• Coax borderline converters to 1st-time purchaseSolution• Analyzed available gam...
Korean Social Game: Conversion rates        © 2009-11 Sonamine LLC.
“Why not just promote to everybody?”Why does tight targeting raise total revenue?• If you spam with conversion/upsell offe...
Chapter 3What are predictives?
Field guide: Metrics v. PredictivesMetrics:                                Predictives:• Measure & report                 ...
From metrics (reporting) to predictions                                   Source:                                   Compet...
The Purpose of PredictivesFocusing promos on those most likely to buy/etc• Communicate to fewer customers  – Reduce opt-ou...
Predictives: the top-ranked decile           concentrates the target behavior         Random selection              Predic...
Predictives for games• Behaviors to predict:  – Conversion         – Churn  – Item purchase      – Viral recommendation  –...
How other industries use predictives• Mobile phone companies  • Who will cancel, or buy a new data plan• Insurance  • Who ...
Chapter 4AT&T Case Study
Case Study: AT&T UpsellOpportunity• Upsell a product to existing customers.Solution• 22 Predictive segments created. Based...
Case Study: Conversion Rates     © 2009-11 Sonamine LLC.
Observations from AT&T Case• The social graph – if available – helps greatly• The combination of behavioral & SNA  outperf...
Chapter 5  Predictives:How they’re done
Pragmatics: What data?What data is used for social-game predictives?1. User-specific (not personal)  –   Demographics (if ...
Tech: Algorithms used•   Neural network, with back propagation•   Support vector machines•   Random forests, with entropy ...
What predictive output looks like                                 Object of prediction                                 (Us...
Case Study: Portal/developer ofmultiplayer casual social games      © 2009-11 Sonamine LLC.
GamePoint: Portal/developer of    multiplayer casual social gamesOpportunity• Get more borderline converters  to make firs...
GamePoint: Conversion rates(160% higher than no promotion)     © 2009-11 Sonamine LLC.
GamePoint: Conversion rates(150% higher than random promotion)                    150%                    higher      Rand...
What went right?Overall increase in conversions…       WHY?• Similar players get similar predictive ratings  – “Marginal c...
GamePoint Case Study: Additional benefitsAutomated continuous campaigns are expected to increase revenue by 10%Incremental...
Tip 1: Plan for multiple, simultaneous,          automated campaignsAd-hoc (one-off) campaigns are not scalable• Promotion...
Tip 2: Customize user experiences.Build scalable use of predictives into your games:• Player-communications: target specif...
Use predictive scoresto customize user experience                 If player is more likely to convert                     ...
Resources• Technical Introduction  – www.wikipedia.org/wiki/Predictive_analytics• Trade show for learning  – www.Predictiv...
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Sonamine GDC Online Presentation On Predicting Player Behaviors

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Introduces predictive analytics to game developers. Tips and lessons from other industries. Case studies showing 63% to 150% higher freemium conversion rates.

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Sonamine GDC Online Presentation On Predicting Player Behaviors

  1. 1. Predicting Player Behaviors:Lessons from TelCo’s & FinanceNick LimCEO, Sonamine
  2. 2. Agenda1. Life cycle management2. Case study: conversion3. What are predictives?4. Case study: AT&T5. Predictives: How they’re done © 2009-11 Sonamine LLC.
  3. 3. Chapter 1Life Cycle Management
  4. 4. Life Cycle Management• Communication tailored to customer’s stage: 1) Welcome & educate. (“Here’s how”) 2) Upsell. Seek referrals 3) Seek renewal, or give retention pitch• How to know what phase they’re in? – Sometimes, it’s easy (first-time player) – Otherwise, predictives usually used
  5. 5. Best Practices from TelcosThese companies learned:• More-engaged customer Easier to upsell• More upselling Lower churnFor best results:• Limit customer communications, and deliver the right message at each stage – Upselling too soon will overwhelm or annoy – Customers are receptive during brief windows
  6. 6. Chapter 2 Case study:Social game conversion © 2009-11 Sonamine LLC.
  7. 7. Case Study: Korean Social Game © 2009-11 Sonamine LLC.
  8. 8. Case Study: Korean Social GameOpportunity• Coax borderline converters to 1st-time purchaseSolution• Analyzed available game-play data• Grouped players into 2 key conversion segments• Showed promo to top predictive segment © 2009-11 Sonamine LLC.
  9. 9. Korean Social Game: Conversion rates © 2009-11 Sonamine LLC.
  10. 10. “Why not just promote to everybody?”Why does tight targeting raise total revenue?• If you spam with conversion/upsell offers… – Players become numbed to your messages – Annoyed, players opt-out, or stop playing (You’ve expedited your churn) – Players are not focused onto the most-appropriate message for their life-cycle stage – You waste money (communication, discounts) – You hurt your reputation & degrade trust
  11. 11. Chapter 3What are predictives?
  12. 12. Field guide: Metrics v. PredictivesMetrics: Predictives:• Measure & report • Estimate & predict the past the future• 100%-certainty possible • Certainty impossible• View correlations • Ratings derived between few variables from 50 or 100 variables © 2009-11 Sonamine LLC.
  13. 13. From metrics (reporting) to predictions Source: Competing on Analytics Davenport & Harris © 2009-11 Sonamine LLC.
  14. 14. The Purpose of PredictivesFocusing promos on those most likely to buy/etc• Communicate to fewer customers – Reduce opt-outs, burn-out, churn• Reach many of the target group – Send the offer to those who want it• Reach others who are similar to the targets – Share the offer with those “on the fence”
  15. 15. Predictives: the top-ranked decile concentrates the target behavior Random selection Predictive ranking Behavior© 2009-11 Sonamine LLC.
  16. 16. Predictives for games• Behaviors to predict: – Conversion – Churn – Item purchase – Viral recommendation – Upsell – Cross-sell• Reach the top predictive segment – Promotions & offers: email, in-game, notifications…
  17. 17. How other industries use predictives• Mobile phone companies • Who will cancel, or buy a new data plan• Insurance • Who will get into accidents• Financial services • Which transaction is fraudulent • Which loan or mortgage will default• Online advertising • Which ad you will click on• Search engines • Which page is most relevant to a query• Public service • Which offenders will again commit that crime © 2009-11 Sonamine LLC.
  18. 18. Chapter 4AT&T Case Study
  19. 19. Case Study: AT&T UpsellOpportunity• Upsell a product to existing customers.Solution• 22 Predictive segments created. Based on: • Loyalty, usage, social-network characteristics.• Mail campaign (promoting the new product) was customized for each segment © 2009-11 Sonamine LLC.
  20. 20. Case Study: Conversion Rates © 2009-11 Sonamine LLC.
  21. 21. Observations from AT&T Case• The social graph – if available – helps greatly• The combination of behavioral & SNA outperforms the sum of their contributions
  22. 22. Chapter 5 Predictives:How they’re done
  23. 23. Pragmatics: What data?What data is used for social-game predictives?1. User-specific (not personal) – Demographics (if available). Location (IP#)2. Game events – Session starts/stops. Achievements, purchases3. Social-graph data – Invites. Gifting. PvP actions. “Visiting”. Etc.
  24. 24. Tech: Algorithms used• Neural network, with back propagation• Support vector machines• Random forests, with entropy reduction• Graph-theoretic methods – Including: social graph analysis• Machine learning
  25. 25. What predictive output looks like Object of prediction (Usually the player) Score, ranking that object Higher score more likely (to convert, churn, buy, etc.) © 2009-11 Sonamine LLC.
  26. 26. Case Study: Portal/developer ofmultiplayer casual social games © 2009-11 Sonamine LLC.
  27. 27. GamePoint: Portal/developer of multiplayer casual social gamesOpportunity• Get more borderline converters to make first-time-purchaseSolution• Analyzed available game play data• Grouped players into 20 conversion segments• Email promo to top segment, with A-B test © 2009-11 Sonamine LLC.
  28. 28. GamePoint: Conversion rates(160% higher than no promotion) © 2009-11 Sonamine LLC.
  29. 29. GamePoint: Conversion rates(150% higher than random promotion) 150% higher Random promo Predictive promo © 2009-11 Sonamine LLC.
  30. 30. What went right?Overall increase in conversions… WHY?• Similar players get similar predictive ratings – “Marginal converters” are rated similarly to inevitable converters.• Promotions go to a smaller group – Less promo-fatigue & irritation; fewer opt-outs – Tightly-targeted emails get huge open rates & CTR
  31. 31. GamePoint Case Study: Additional benefitsAutomated continuous campaigns are expected to increase revenue by 10%Incremental revenue: 5x greater than investment © 2009-11 Sonamine LLC.
  32. 32. Tip 1: Plan for multiple, simultaneous, automated campaignsAd-hoc (one-off) campaigns are not scalable• Promotions should be ongoing & customized• Requirements: • Ability to deliver user-specific messages • Real-time delivery of user rankings • Offers for each stage of life cycle © 2009-11 Sonamine LLC.
  33. 33. Tip 2: Customize user experiences.Build scalable use of predictives into your games:• Player-communications: target specific players• Game-play: behavior based on player ID• Context: ads (e.g.) based on player ID• Engineering: allow individualized communication• A-B testing: systems must be easy to re-target © 2009-11 Sonamine LLC.
  34. 34. Use predictive scoresto customize user experience If player is more likely to convert -Turn off 3rd party ads - Offer a promo (a discount) If player is less likely to convert - Turn on ads -Turn on cross-promo bar © 2009-11 Sonamine LLC.
  35. 35. Resources• Technical Introduction – www.wikipedia.org/wiki/Predictive_analytics• Trade show for learning – www.PredictiveAnalyticsWorld.com• Myths and pitfalls – www.information-management.com/specialreports/20050503/ 1026882-1.html• Sonamine information, slides, and whitepaper – www.Sonamine.com © 2009-11 Sonamine LLC.
  36. 36. For more about predictivesand Sonamine’s free trial program

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