1. 28 September 2012

2. Agenda
Background
Current state of analytics
Analytics 2.0
Summary

4. Lloyd Melnick

5. fiveonenine games

6. Publishing and Developing
Launched publishing
program in August
Campaign Story launched &
Several projects in the pipeline

7. More stars than the Miami Heat
 Ex-Studio Head of EA’s  Gardens of Time
North Carolina office  Shutter Island
 Member founding  City of Wonder
management team at  Mortimer Beckett
Motricity and Appia
 Big City Adventure
 Team lead at RIM’s NC
studio  Madden 3DS
 20 year veteran of  Nascar Kart Racing Wii
IBM/Microsoft/Citrix  Woodland Heroes
 GM Europe and LATAM
at Disney/Playdom

9. Analytics and gaming
Statistical Tools
Predictive Models
Data Mining
In-Game
Analytics Tools
9

10. Standard Dashboard
 Where your game currently is
 Kontagent, Mixpanel, Honeytracks, Flurry, Apsalar, Co
llectTM & MeasureTM (by GamesAnalytics)
 SWRVE & other A/B testing solutions

11. Ad hoc reports
 Addresses immediate concerns and issues
 Kontagent, Mixpanel, Honeytracks

12. Query/drill down
 Where exactly is the problem?
 Kontagent, Mixpanel, Honeytracks, CollectTM &
MeasureTM (by GamesAnalytics)

13. Alerts
 What actions are needed

15. STATISTICAL Analysis
 Correlations & CHI SQ
Are there statistically significant associations among factors
 T-tests & ANOVA
Are there statistically significant differences among groups in
usage or monetization
 Regression Analysis
What are the factors (i.e.: gender, age) “significantly” impact
revenue and by how much?
 Outlier Analysis
Are there users playing or monetizing far differently than the
most?
 Excel, R, SAS, SPSS, STATA, SWRVE, PredictTM
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16. Forecasting
• Time Series Analysis
• How much revenue will we bring in next quarter?
• How many users will we have in the future (near term)?
• What will the load on the servers be?
• Excel, R, SAS, SPSS, STATA
16

17. Predictive Modeling
 Logistic Regressions, Decision Trees, etc.
• Who is more likely to monetize?
• Who is more likely to react to in game messaging?
 Survival Analysis
• What will be the lifetime of a user?
• How long will it take for users to monetize? (time to first
purchase)
• What factors impact the retention of the users
 R, SAS, SPSS, STATA, PredictTM
17

18. Data mining
• Clustering (Segmentation) Analysis
• Are there clear “segments” among our users that could be
approached differently?
• Association Analysis
• Are there items that sell “together”?
• SAS Enterprise Miner, SPSS
Modeler, Weka, Playnomics, HoneyLizer, PredictTM
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19. Simulation
• Monte–Carlo Simulation
• How long does our game take on average?
• How many turns on average will the players need to finish?
• What happens if we tweak the rules of the game?
• Excel, R, Risk Solver, SAS, SPSS, STATA

20. Optimization
• Price Optimization
• What is the optimal price for the virtual goods?
• Linear Programing
• What is the optimal allocation of recourses for supporting the
game?
• R, Risk Solver, Oracle Cristal Ball, SAS

21. Analytics design – most important
metric
CUSTOMER
LIFETIME
VALUE
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22. Case Study- Results
Our partner GamesAnalytics for BBC Worldwide
 Retention rate improved by 95%
 Revenues increased by 40%
 Over 2m registrations
 Ranked 2nd in App Store

24. Summary
Analytics give you a great picture of
where your game is
They help make production and
marketing decision
They can help to shape up fact based
strategy

25. Thank you
Aren Arakelyan
Aren.Arakelyan@fiveoneninegames.com
Lloyd Melnick
Lloyd.melnick@fiveoneninegames.com
http://lloydmelnick.com/