Presentation from Casual Connect Seattle 2012 on The Importance of the Future, new opportunities to use business intelligence in the social gaming space. Analytics is much more than data collection and dashboards. Predictive modeling enables behavioral segmentation that provides actionable insights not only through marketing but also game design to deliver greater player satisfaction.

1. THE IMPORTANCE OF THE
FUTURE
Lloyd Melnick
Casual Connect
2012

2. Agenda
Background
Current state of analytics
Analytics 2.0
Summary

3. BACKGROUND

4. Lloyd Melnick

5. FiveOneNine Games
Capitol FiveOneNine
EW Scripps
Broadcasting Games

6. Our Business Strategy
GAME GAME
DEVELOPER PUBLISHER
• Campaign Story • Publishing
launches next Program
month launched this
week

7. Analytics and gaming
Statistical Tools
Predictive Models
Data Mining
In-Game
Analytics Tools
7

8. Analytics and Gaming
In-Game
Statistical Tools
Analytics
(Predictions)
(Dashboards)
• What • What will
Happened? Happen?

9. Customer Lifetime Value
Most Important Metric
Future Better
Current Business
Predicted
Lifetime Decisions
Lifetime
Value
Value $$$$

10. CURRENT STATE OF ANALYTICS

11. In-Game Analytics Tools
DAU
Session
K-Factor
Times
Lifetime
Virtual
2-day
Goods
retention
Sold
7-day
retention
Visualization of Game Metrics
11

12. In-Game Analytics
• Easy Visualization of high level metrics
Executive Dashboards
• Ability to address immediate concerns
Ad-Hoc Reports
• Where exactly is the problem?
Query/Drilldown
• What actions are needed?
Alerts
Kontagent, Mixpanel, Honeytracks, CollectTM & MeasureTM
(by GamesAnalytics) 12

13. ANALYTICS 2.0

14. STATISTICAL Analysis
• Are there statistically significant
Correlations and CHI associations among factors?
SQ
• Are there statistically significant
T-tests & ANOVA differences among groups in usage or
monetization?
• What are the factors (i.e.: gender, age)
Regression Analysis that “significantly” impact revenue and
by how much?
• How are the high value players
Outlier Analysis monetizing differently than most
players?
Excel, R, SAS, SPSS, STATA, SWRVE, PredictTM
14

15. Forecasting
• How much revenue will we bring in next
quarter?
Time Series Analysis • How many users will we have in the
future (near term)?
Excel, R, SAS, SPSS, STATA
15

16. Predictive Modeling
• Who is more likely to monetize?
Logistic • Who is more likely to react to in-game
Regressions, Decision messaging?
Trees, etc.
• What will be the lifetime of a user?
• How long will it take for users to
Survival Analysis monetize? (time to first purchase)
• What factors impact the retention of
the users?
R, SAS, SPSS, STATA, PredictTM, MeasureTM
(by GamesAnalytics) 16

17. Data Mining
• Are there clear “segments” among our
Clustering users that could be approached
(Segmentation) Analysis differently?
• Are there items that sell “together”?
Association Analysis
• How do users feel about our games?
• What are the main topics of conversation
Text Mining (Consumer for our Twitter followers?
Sentiment Analysis) • Are comments on our Facebook page
mostly positive or negative?
SAS Enterprise Miner, SPSS Modeler, Weka, PredictTM
17

18. Simulation
• How long do tasks in our game take to
play on average?
Monte–Carlo • What happens if we tweak the rules of
Simulation the game?
Excel, R, Risk Solver, SAS, SPSS, STATA
18

19. Optimization
• What is the optimal price for the virtual
goods?
Price Optimization
• What is the optimal allocation of
resources for supporting the game?
Linear Programing
R, Risk Solver, Oracle Cristal Ball, SAS
19

20. SUMMARY

21. Huge opportunities to use analytics
better
Analytics is the foundation of business and games deep
insight
They help you optimize production and marketing
decisions
They help improve the game and the user experience
Adding predictive modeling to in-game analytics lays the
foundation for additional optimization of business decisions

22. Thank you!
Lloyd.melnick@fiveoneninegames.com
Twitter: Lloyd Melnick
http://lloydmelnick.com/