Sonamine GDC Online Presentation On Predicting Player Behaviors

Predicting Player Behaviors:
Lessons from TelCo’s & Finance
Nick Lim
CEO, Sonamine

Agenda
1. Life cycle management
2. Case study: conversion
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 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:
Social game conversion


Case Study: Korean Social Game


Case Study: Korean Social Game

Opportunity
• Coax borderline converters to 1st-time purchase

Solution
• Analyzed available game-play data
• Grouped players into 2 key conversion segments
• Showed promo to top predictive segment


Korean Social Game: Conversion rates


“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

Chapter 3

What are predictives?

Field guide: Metrics v. Predictives
Metrics: 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


From metrics (reporting) to predictions

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
• 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”

Predictives: the top-ranked decile
concentrates the target behavior
Random selection Predictive ranking

Behavior

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…

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


Case Study: AT&T Upsell
Opportunity
• 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

Case Study: Conversion Rates


Observations from AT&T Case
• The social graph – if available – helps greatly

• The combination of behavioral & SNA
outperforms 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#)
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
– Including: social graph analysis
• Machine learning

What predictive output looks like

Object of prediction
(Usually the player)

Score, ranking that object
Higher score more likely
(to convert, churn, buy, etc.)


Case Study: Portal/developer of
multiplayer casual social games


GamePoint: Portal/developer of
multiplayer casual social games
Opportunity
• Get more borderline converters
to make first-time-purchase

Solution
• Analyzed available game play data
• Grouped players into 20 conversion segments
• Email promo to top segment, with A-B test

GamePoint: Conversion rates
(160% higher than no promotion)


GamePoint: Conversion rates
(150% higher than random promotion)

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
inevitable converters.
• Promotions go to a smaller group
– Less promo-fatigue & irritation; fewer opt-outs
– Tightly-targeted emails get huge open rates & CTR

GamePoint Case Study: Additional benefits

Automated continuous campaigns are expected to
increase revenue by 10%

Incremental revenue: 5x greater than investment


Tip 1: Plan for multiple, simultaneous,
automated campaigns

Ad-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


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


Use predictive scores
to 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

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


For more about predictives
and Sonamine’s free trial program

Sonamine GDC Online Presentation On Predicting Player Behaviors

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