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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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Predicting Player Behaviors:
Lessons from TelCo’s & Finance
Nick LimCEO, Sonamine
Agenda
1. Life cycle management
2. Case study: conversion
3. What are predictives?3. What are predictives?
4. Case study: AT&T
5. Predictives: How they’re done
© 2009-11 Sonamine LLC.
Chapter 1
Life Cycle Management
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 pitch3) 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
Best Practices from Telcos
These companies learned:
• More-engaged customer � Easier to upsell
• More upselling � Lower churn
For 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
Chapter 2
Case study:
© 2009-11 Sonamine LLC.
Case study:
Social game conversion
Case Study: Korean Social Game
© 2009-11 Sonamine LLC.
Opportunity
• Coax borderline converters to 1st-time purchase
Case Study: Korean Social Game
Solution
• Analyzed available game-play data
• Grouped players into 2 key conversion segments
• Showed promo to top predictive segment
© 2009-11 Sonamine LLC.
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 offers…
– Players become numbed to your messages
– Annoyed, players opt-out, or stop playing– 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
Chapter 3
What are predictives?What are predictives?
Field guide: Metrics v. Predictives
Metrics:
• Measure & report
the past
• 100%-certainty possible
Predictives:
• Estimate & predict
the future
• Certainty impossible• 100%-certainty possible
• View correlations
between few variables
• Certainty impossible
• Ratings derived
from 50 or 100 variables
© 2009-11 Sonamine LLC.
Source:
From metrics (reporting) to predictions
© 2009-11 Sonamine LLC.
Source:
Competing on Analytics
Davenport & Harris
The Purpose of Predictives
Focusing promos on those most likely to buy/etc
• Communicate to fewer customers
– Reduce opt-outs, burn-out, churn– 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”
Random selection
Predictives: the top-ranked decile concentrates the target behavior
Predictive ranking
© 2009-11 Sonamine LLC.
Behavior
Predictives for games
• Behaviors to predict:
– Conversion – Churn
– Item purchase – Viral recommendation
– Upsell – Cross-sell– Upsell – Cross-sell
• Reach the top predictive segment
– Promotions & offers: email, in-game, notifications…
• 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
How other industries use predictives
• 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.
Chapter 4
AT&T Case StudyAT&T Case Study
Opportunity
• Upsell a product to existing customers.
Case Study: AT&T Upsell
© 2009-11 Sonamine LLC.
Solution
• 22 Predictive segments created. Based on:
• Loyalty, usage, social-network characteristics.
• Mail campaign (promoting the new product)
was customized for each segment
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
outperforms the sum of their contributionsoutperforms the sum of their contributions
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 available). Location (IP#)– Demographics (if available). Location (IP#)
2. Game events
– Session starts/stops. Achievements, purchases
3. Social-graph data
– Invites. Gifting. PvP actions. “Visiting”. Etc.
Tech: Algorithms used
• Neural network, with back propagation
• Support vector machines
• Random forests, with entropy reduction
• Graph-theoretic methods• Graph-theoretic methods
– Including: social graph analysis
• Machine learning
Object of prediction
(Usually the player)
What predictive output looks like
© 2009-11 Sonamine LLC.
Score, ranking that object
Higher score � more likely(to convert, churn, buy, etc.)
Case Study: Portal/developer of
multiplayer casual social games
© 2009-11 Sonamine LLC.
Opportunity
• Get more borderline converters
to make first-time-purchase
GamePoint: Portal/developer of
multiplayer casual social games
Solution
• Analyzed available game play data
• Grouped players into 20 conversion segments
• Email promo to top segment, with A-B test
© 2009-11 Sonamine LLC.
GamePoint: Conversion rates
(160% higher than no promotion)
© 2009-11 Sonamine LLC.
150%
GamePoint: Conversion rates
(150% higher than random promotion)
© 2009-11 Sonamine LLC.
150%higher
Random promo Predictive promo
What went right?
Overall increase in conversions… WHY?
• Similar players get similar predictive ratings
– “Marginal converters” are rated similarly to – “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
Automated continuous campaigns are expected to
increase revenue by 10%
GamePoint Case Study: Additional benefits
increase revenue by 10%
Incremental revenue: 5x greater than investment
© 2009-11 Sonamine LLC.
Ad-hoc (one-off) campaigns are not scalable
• Promotions should be ongoing & customized
Tip 1: Plan for multiple, simultaneous,
automated campaigns
• 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.
Build scalable use of predictives into your games:
• Player-communications: target specific players
• Game-play: behavior based on player ID
Tip 2: Customize user experiences.
• 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.
If player is more likely to convert
-Turn off 3rd party ads
- Offer a promo (a discount)
Use predictive scores
to customize user experience
© 2009-11 Sonamine LLC.
If player is less likely to convert
- Turn on ads
-Turn on cross-promo bar
Resources
• Technical Introduction– www.wikipedia.org/wiki/Predictive_analytics
• Trade show for learning– www.PredictiveAnalyticsWorld.com– 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.
For more about predictives
and Sonamine’s free trial program