This is a presentation I gave at the Montreal International Game Summit on November 2, 2011.

1. Would you like some
data with
your coffee?
Caryl D Shaw
@caryl_shaw

2. Hello

4. Restaurants
Busgirl
Waitress
Bartender
Catering Cook
Prep Cook
Line Cook

5. Production Monkey
Advertising Tech Manager
Operations Director
Online Team Manager
Restaurants Video Game Producer
Busgirl Live Producer
Waitress
Tech & Games
Bartender
Catering Cook
Prep Cook
Line Cook

13. What does coffee have
to do with Analytics?

14. !"#$%&'&()&$

15. Planting
Harvesting
Storing/Processing
!"#$%&'&()&$
Preparing
Consuming
Enjoying

16. • Instrumentation
• Reading the beans
• Taking action

17. When do you add
Analytics to your game?
• After initial game design
• Before Alpha
• And before DB design for
your game is locked

18. Hey, is this thing on?
• Do controlled tests early
• Verify against another data source later
• Don’t forget gameplay... ever.

19. Technical
Considerations
• Use a data format that you can add to later
- like JSON
• Pick a DB that allows you to add fields
without downtime - like MongoDB
• If you have lots of $$: consider Hadoop

20. Pro tips!
• Go to the source
• Make it user-friendly
• Smoke tests are your
friends
• Have a contingency
plan for data spikes

21. • You just need the basics
Light • Small team, no dedicated resource
• Not basing decisions on Analytics
• Got a taste and wants more
Medium • Dedicated Analytics resource(s)
• Considers data in decision-making
• Deep Analytics use
Dark • Dedicated Analytics team
• Building business around Analysis

22. Light
• Focus on usage basics
• DAUs/MAUs
• New & Returning Users
• Paying Users
• Less about tracking and analyzing and more
about reporting

23. Medium
• Focus on analysis
• Break users into cohorts
• # of Sessions/Length of Sessions
• User Funnel Analysis
• Using analysis to alter game features and
content to shift player behavior and
improve revenue

24. Dark
• Building your business around what you
learn from Analytics
• Cross-game, cross-platform statistics
tracking
• Tracking long-term player behavior
• Building smart algorithms to predict
game economy shifts

25. Light Medium Dark
• Advertising Reports
• App Store Downloads • Acquisition Path • Cohort grouping
Reach • # of App launches • Day-on-day comparisons • Realtime updates
• New Registrations • Some demographic data • Extensive demographic data
• Competitive analysis
• # of Unique Devices
• Sort by level distribution (if level • Lifetime value of a customer
• # of Returning Users
Retention • # of Paying Customers
based game) • Data broken down by Active vs.
• Return rate break down (1 day, 3 Inactive
day, 7 day, etc)
• A/B Testing
• Track # of Sessions • User Segmentation
• Level Distribution across
• Track Session Duration
Engagement all stats
• Track player behavior to resolve
• Game Feature tracking - game
currency, XP, etc
• Tutorial Funnel behavior
churn problems • Friend invites (and subsequent
engagement)
• Most Purchased Items • Revenue for social vs.
• ARPU • Highest Grossing Items independent actions
Revenue • ARPPU • First Purchase vs. Repeat • User path tracking to paying
Purchase moment
• Device Type • Debugging/Logging Statistics
Player • Customer Service
requests
• OS Version • Cases opened
Relations • Returns
•
•
Refunds
Social Media traffic metrics
•
•
Cases closed
Cases by type

26. Breaking it down
• Reach
• Retention
• Engagement
• Revenue
• Player Relations

27. Reach

28. Reach
App Store Acquisition Path Advertising Reports
Downloads
Day-on-day Cohort grouping
# of App launches Comparisons
Realtime updates
New Registrations Some demographic
Data Extensive
demographic data
Competitive Analysis

29. Reach

30. Retention

31. Retention
# of Returning Users # of Unique Devices Lifetime value of a
Sort by level customer
# of Paying distribution (if level
Customers based game) Data broken down by
Active vs. Inactive
Return rate break
down (1 day, 3 day, 7
day, etc)

32. Retention
!" #" $" %" &" '" (" )" *" !+" !!" !#" !$" !%" !&" !'" !(" !)" !*" #+" #!" ##" #$" #%" #&" #'" #(" #)" #*" $+" $!" $#" $$" $%" $&" $'" $(" $)" $*" %+" %!" %#" %$" %%" %&" %'" %(" %)" %*" &+"

33. Engagement

34. Engagement
Level Distribution Track # of Sessions A/B Testing
across all stats
Track Session User Segmentation
Tutorial Funnel Duration
behavior Game Feature
Track player behavior tracking - game
to resolve churn currency, XP, etc
problems
Social invitation
tracking

35. Engagement

36. Revenue

37. Revenue
ARPU Most Purchased Revenue for social
Items vs. independent
ARPPU actions
Highest Grossing
Items User path tracking to
paying moment
First Purchase vs.
Repeat Purchase

38. Revenue
$60,000
$50,000
$40,000
Revenue
Item C ($4.99)
$30,000
Item B ($2.99)
Item A ($0.99)
$20,000
$10,000
$-
9/18/11 9/19/11 9/20/11 9/21/11 9/22/11 9/23/11 9/24/11

39. Customer
Relations

40. Customer
Relations

41. You have all this
information.
What do you do with it?

42. `

44. Mojo purchases slowed
down on the feature
Percent paying and
ARPU decreasing

45. Results of changes
Hoorah!
Ack!

46. Tuning for fun and profit
• Test your tuning levers!
• Can you:
• Control the rates your customers earn XP?
• Control item costs and rewards?
• Tune virtual currency to in-game currency
ratios?
• Do you have controls to help encourage
longer session durations or multiple sessions?

47. Your
analytics
options

48. What is your
core competency?
What do you
want it to be?

49. Build your own
• Choose your own tech
• Total control over your data
• Scalability at will

50. Build your own
• Choose your own tech
• Total control over your data
• Scalability at will

51. If you build it, hire
these people:
• BRAINIACS
• People who love to visualize data
• Experts in scalability, who also track the
latest technology
• People who love people

52. Free Analytics
iOS, Android, WP, BB, J2ME
App Developers: 52,000
Live Applications: 115,000
Devices per month: 330M
Sessions per month: 19 B
Events per month: 205 B
AppCircle Network
iOS and Android
App Developers: 2,100
Devices per month: 150 M

53. • Schema-less like data model
• Pattern-based data processing
• Identifies patterns in massive data sets.

55. • What Zynga uses
• Vertica is the backend software
• Tableau is the visualization tool

56. Last words
• Instrumentation is the easiest
thing to push off until late in
development. Don’t do that.
• Know who your customers are
• This is the science. Don’t let it
ruin the art.

57. Thanks!
Caryl D. Shaw
@caryl_shaw