Embed Size (px)
Citation preview
A Case for Predictive Analytics
28 September 2012
AgendaBackground
Current state of analytics
Analytics 2.0
Summary
Background
Lloyd Melnick
fiveonenine games
Publishing and Developing
Launched publishing program in August
Campaign Story launched & Several projects in the pipeline
More stars than the Miami HeatEx-Studio Head of EA’s
North Carolina officeMember founding
management team at Motricity and Appia
Team lead at RIM’s NC studio
20 year veteran of IBM/Microsoft/Citrix
GM Europe and LATAM at Disney/Playdom
Gardens of TimeShutter IslandCity of WonderMortimer BeckettBig City AdventureMadden 3DSNascar Kart Racing
WiiWoodland Heroes
Current State
9
Analytics and gaming
In-Game Analytics Tools
Statistical ToolsPredictive ModelsData Mining
Standard DashboardWhere your game currently isKontagent, Mixpanel, Honeytracks, Flurry,
Apsalar, CollectTM & MeasureTM (by GamesAnalytics)
SWRVE & other A/B testing solutions
Ad hoc reportsAddresses immediate concerns and issuesKontagent, Mixpanel, Honeytracks
Query/drill downWhere exactly is the problem?Kontagent, Mixpanel, Honeytracks, CollectTM
& MeasureTM (by GamesAnalytics)
AlertsWhat actions are needed
Analytics 2.0
15
STATISTICAL Analysis Correlations & CHI SQAre there statistically significant associations among factors T-tests & ANOVAAre there statistically significant differences among groups in usage or monetization Regression AnalysisWhat are the factors (i.e.: gender, age) “significantly” impact revenue and by how much? Outlier AnalysisAre there users playing or monetizing far differently than the most? Excel, R, SAS, SPSS, STATA, SWRVE, PredictTM
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
17
Predictive ModelingLogistic 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
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
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
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
Case Study- ResultsOur partner GamesAnalytics for BBC Worldwide
Retention rate improved by 95%Revenues increased by 40%Over 2m registrationsRanked 2nd in App Store
Summary
SummaryAnalytics 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
Thank you
Aren Arakelyan
http://lloydmelnick.com/
Lloyd Melnick
