sonamine gdc online presentation on predicting player behaviors

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:


Case study:

Social game conversion

Case Study: Korean Social Game


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


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– 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


Source:

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– 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


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


Chapter 4

AT&T Case StudyAT&T Case Study

Opportunity

• Upsell a product to existing customers.

Case Study: AT&T Upsell


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 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


Score, ranking that object

Higher score � more likely(to convert, churn, buy, etc.)

Case Study: Portal/developer of

multiplayer casual social games


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


GamePoint: Conversion rates

(160% higher than no promotion)


150%

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 – “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


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


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


If player is more likely to convert

-Turn off 3rd party ads

- Offer a promo (a discount)

Use predictive scores

to customize user experience


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


For more about predictives

and Sonamine’s free trial program

sonamine gdc online presentation on predicting player behaviors

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