ARM Model - Casual Connect

Report
Actionable Analytics:
User Economics in Game Development
Jason Lee | Sr. Manager, Customer Success APAC
AGENDA
• What is Kontagent?
• What is data driven
development?
• User Economics:
The A.R.M. Model
• Deep Data Exploration
• Q&A
2
WHAT IS
KONTAGENT?
We are the leading user
analytics platform for the
social and mobile web.
User-Centric Data
Accessibility
Domain Expertise
1
Kontagent Facts
• Founded in 2007
• 100+ employees and growing
• Locations around the globe
• 1000’s of Apps Instrumented
• Over 60 Billion Events/Mo Tracked
• 200M+ MAUs tracked
• Track $1 of every $4 spent in the
social gaming industry
3
What is
data driven
development?
5
User Economics –
The A.R.M. Model
• Track users from point
of acquisition through
monetization
 Acquire quality users
at lowest cost
 Keep users engaged
as long as possible
 Get them to spend as
much as possible
6
A.R.M. - Acquisition
GOAL
WHAT TO
MEASURE
• Identify profitable user segments – acquire as many at
the lowest cost possible
• CAC
• Retention
• ARPU
* All of the above per channel and per campaign
7
Acquisition:
Analyze in-game behavior to optimize ad spend
6
A.R.M. – Retention (and Engagement)
GOAL
WHAT TO
MEASURE
• Increase user lifetime playing games – highly retained
and engaged customers are worth more
•
•
•
•
Avg. Session length and # of Sessions per day
DAU / MAU (Stickiness)
In-app funnel conversion
1, 7 and 30 day Retention
9
Retention:
Retention rates important to monitor
But need to dig deeper to take action on data collected…
6
Retention:
Measure the actions that drive your game
6
Retention:
Optimize user paths at key conversion points
6
A.R.M. – Monetization
GOAL
WHAT TO
MEASURE
• Harvest retained users – maximize lifetime value of
users over games’ lifecycle
•
•
•
•
Points of user monetization
A/B Test currency bundles
% Paying Users (PPU)
ARPU & ARPPU
13
Monetization:
Identify trends in PPU, ARPU and ARPPU
6
Monetization:
Optimize when users monetize
6
Monetization:
A/B Test payment methods and packaged bundles
6
Data Exploration
GOAL
WHAT TO
MEASURE
• Gain competitive advantage by exploring edge cases
and deep user behaviors specific to your games
• Cross-app / Cross-platform user behaviors
• Whale analysis – identify high spenders
• Custom LTV models
17
Data Exploration:
Ad hoc queries across entire collected data-set
6
Conclusion
• Continually collect and analyze data to validate
design decisions
• Identifying trends is important – but taking action
requires deep understanding of game specific data
19
QUESTIONS?
20

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