This document discusses analytics and its use for game companies. It covers why analytics are needed, different types of analytics like reporting, predictive analytics and optimization. It also discusses implementation options like building analytics in-house versus using analytics software as a service. The document includes case studies on how analytics were used to improve first-time user conversions and retention. It discusses evaluating the return on investment of analytics and prioritizing analytic efforts. Finally, it introduces some of Sonamine's analytic products and services.

1. Nick Lim
nick@sonamine.com
Sonamine

2.  Why do we need analytics?
 Making sense of “analytics”
 Vendors, implementation options
 How to use analytics
 Case study – First time spender conversion
 Case study – Retention
© 2009-11 Sonamine LLC. Confidential and proprietary

3.  Persistent player profiles
 Players pay you over time
 Connected games
 Free to play
 Telecoms, banks, retailers have been doing
this for a while now…
© 2009-11 Sonamine LLC. Confidential and proprietary

4. Optimization
What’s the best that can happen?
FUTURE Predictions
What will happen next?
Forecasting
What if these trends continue?
Statistical analysis
Why is this happening?
Value
Alerts
What just happened?
Query / drill-down PAST
Where is the problem?
Ad hoc reports
How many, how often, where?
Standard reports
What happened? Source: Competing on Analytics Davenport & Harris
Degree of Intelligence
© 2009-11 Sonamine LLC. Confidential and proprietary

5. Optimization
Automatic, What’s the best that can happen?
machine learning Predictions
What will happen next?
Forecasting
What if these trends continue?
Statistical analysis
Why is this happening?
Value
Alerts
What just happened?
Query / drill-down Human
Where is the problem? analyst
Ad hoc reports and
How many, how often, where? interactive
Standard reports
What happened? Source: Competing on Analytics Davenport & Harris
Degree of Intelligence
© 2009-11 Sonamine LLC. Confidential and proprietary

6. Optimization
Hundreds / thousands of What’s the best that can happen?
variables crunched by Predictions
machine What will happen next?
Forecasting
What if these trends continue?
Statistical analysis
Why is this happening?
Value
< 20
Alerts variables
What just happened?
processed
Query / drill-down by human
Where is the problem? at one
Ad hoc reports time
How many, how often, where?
Standard reports
What happened? Source: Competing on Analytics Davenport & Harris
Degree of Intelligence
© 2009-11 Sonamine LLC. Confidential and proprietary

7. Optimization
What’s the best that can happen?
Predictions User scoring, machine learning
What will happen next?
Forecasting
What if these trends continue?
Statistical analysis User segmentation, machine
Why is this happening? learning
Alerts Real Time, monitoring
What just happened?
Query / drill-down Filtering, slice and dice, group by,
Where is the problem? page by
Ad hoc reports Custom Segmentation, on-the-fly,
How many, how often, where? cohort analysis
Standard reports Dashboards, cohort reports,
What happened?
Source: Competing on Analytics Davenport & Harris
© 2009-11 Sonamine LLC. Confidential and proprietary

8. Actions
• Optimization
• Predictions
• Forecasting
• Stats
Reporting
• Alerts
• Queries
• Ad hoc
• Canned
Analytics Analytics SaaS Services
softwareSonamine LLC. Confidential and proprietary
© 2009-11

9.  Pros
 Data control, no chance of player cannibilization
 Flexible and customizable
 No added costs for multiple games
 Cons
 Complexity
 High initial startup costs
 Maintenance costs
© 2009-11 Sonamine LLC. Confidential and proprietary

10.  Pros
 Quick startup
 Deep functionality for pure play vendor
 Integration with “taking action” for services vendors
 Cons
 Many SaaS providers require co-ownership of data
 Possible player cannibalization by other customers of
SaaS vendor
 Linear cost increase with more games
© 2009-11 Sonamine LLC. Confidential and proprietary

11.  Change your behavior
 Better user acquisition sources
 Game design
 Change player behavior (CRM)
 More effective targeting of monetization
campaigns
 More effective targeting of retention campaigns
© 2009-11 Sonamine LLC. Confidential and proprietary

12. © 2009-11 Sonamine LLC. Confidential and proprietary

13. Opportunity
Get more borderline converters to actually
make first-time-purchase
Solution
Analyzed available game play data to group
players into 20 different conversion segment.
Sent email promotion to top segment with A-B
test.
© 2009-11 Sonamine LLC. Confidential and proprietary

14. 160%
Higher
No promo Sonamine promo
© 2009-11 Sonamine LLC. Confidential and proprietary

15. 150%
higher
Random promo Sonamine promo
© 2009-11 Sonamine LLC. Confidential and proprietary

16. Automated campaigns could increase new
conversions by 10%
Additional revenue 5x greater than investment
© 2009-11 Sonamine LLC. Confidential and proprietary

17. © 2009-11 Sonamine LLC. Confidential and proprietary

18. Opportunity
Get more borderline churners to stay in game
Solution
Analyzed available game play data to group
players into 20 different conversion segment.
Show in game promo to likely churners, using
A-B test to gauge results.
KPIs are change in engagement metrics after 1
week of campaign.
© 2009-11 Sonamine LLC. Confidential and proprietary

19. Sonamine promo 1
72%
Sonamine no promo
11%
0
Avg level up / player Users with virtual transactions Monetized users
-30%
-86%
 Accurate predictions.
 Pop up promotion increases retention, while not leaving
money on the table by offering promo to all players.
© 2009-11 Sonamine LLC. Confidential and proprietary

20.  What is the Return on Analytics?
 Improvement in revenue/cost/profit from analytic
decision
 Cost of all analytics = data
collection, tools, analysts, services
 Prioritize analytic efforts
 Which type moves the needle, or highest ROA?
© 2009-11 Sonamine LLC. Confidential and proprietary

21.  ConvertSoon™
 Automated list of players most likely to make a first
time purchase
 ChurnSoon™
 Automated list of players most likely to abandon the
game
 PurchaseMoreSoon™
 Automated list of players most likely to purchase
more items
 InfluenceSoon™
 Automated list of players most likely to influence their
friends.
© 2009-11 Sonamine LLC. Confidential and proprietary