Optimization Technologies for Lifting KPIs in Games

Alan Avidan, PhD, MBAExec. Director & Chief BeesDev

Optimization Technologiesfor Lifting KPIs in Games

2012© Bees and Pollen

• Analytics• A/B Testing• A Priori Segmentation • Clustering Segmentation

Optimization Technologies We’ll Cover


Game Elements/Events

3

Payment Page TutorialPromotions

Landing page

Full tutorial

Level 2


Payment Page

4

Which one produces more revenue?

Low Range High Range


Promotions

5

Which one performs better?

Percent Discount Absolute Discount


Tutorial/Game Flow

6

Option 1 Option 2Landing page

Full tutorial

Level 2

Landing page

Short tutorial

Level 2

Option 3Landing page

No tutorial

Level 1


• Payment Page • Game flow/tutorial • Promotions and offers• Sharing Messages • Virtual Goods Selections • Payouts (Casino Games) • Themes/Arts/Creatives• Landing Pages• Emails (Subject, Body, Submission)• Special Deals Closing Time

More Elements Can be Optimized


Decide

Measure

DisplayAnalyze

Change

Analytics


A/B Testing

Define options

Split traffic Measure results Deploy winner Result

Low range

high range

high range


Make sure that the test is statistically significant - run it for long enough, and with enough traffic to make it count

I have learned how dramatically, and ridiculously wrong my most basic assumptions were

It's empirically proven that you should let the data tell you what works or not and you should constantly be testing

That the devil is in the detail - a minor change can generate a significant result

Experts Weigh In: A/B Testing

Q: What are the most unexpected things people have learned from A/B tests?


Upside• Simple; understandable

Can achieve good results

Downside:• One-size-fits-all• Results deteriorate over time

A/B Testing – Bottom line


Define segments Define Options and rule base

Result

A Priori Segmentation

Low range

high range


A Priori Segmentation Upside• Can be effective if segmentation was meaningful

Downside• Segments are predefined and remain unchanged

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


Clustering Segmentation Define options

A/B test options

Segment users based on result

Deploy winner

Low range

High range


Clustering Segmentation

Upside:• Highest Lift • Discover correlations never knew existed

Downside:• Requires storage of terabytes of data• Need really smart people• Effort = High


Real-time and automated predictive algorithmic technology that serves each user the page options they are most likely to convert on

Clustering SegmentationPredictive Best-Fit


Advanced algorithms find correlations between user data DNA and conversions

Predictive Best-Fit

UserUser Social and Behavioral Data

User DNA Generation

Predictive Best-Fit Algorithms


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Thank You!

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Backup slides

19


Geo-Demographic attributes: age, gender, education, country

Facebook attributes: Friends, Likes, Interests, Posts, Events

Behavioral attributes: level, spending, score, progress, custom

Session attributes: time of day, day, duration

Proprietary attributes: novice, high-bidder, risk-averse

3rd Party attributes: income level, education

Attribute Sources


• Automated end-to-end solution• Machine self-learning• Real-time• No game history required• Numerous data sources• Dashboard – Easy to swap-in options• Deep new insights (identify discriminators)

Predictive Best-Fit All the gain without the pain

Optimization Technologies for Lifting KPIs in Games

Technology

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