20 minute intro to analytics in free-to-play games for YLE's Drupal team and Alkuvoima

1. What can we learn from
game analytics?

2. Hello
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Osma Ahvenlampi, founder, Metrify.io
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Formerly CTO of Sulake: Habbo Hotel
Analytics & monetization expert, advisor, consultant
Metrify does Operational Data Science
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extracting continuous, automated value from business data

3. Analytics changed games forever
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Games used to be almost completely unmonitored and analyzed once
released to the market
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That, is, analysis done on them was desktop reverse engineering
Today, they’re among the most comprehensively analyzed products
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Because they can be: fully digital, open platforms, online play
Because they have to be: free-to-play kills inefficient products
Play data shapes games through their lifetime

4. The four key metrics of free products
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Acquisition: where, how & at what cost can new users be found
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Retention: how many stay over a period of time
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Engagement: how much time do people consume
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Conversion: how often does all of the above lead to revenue
Without Engagement, this is referred to as the “ARC” metric

5. Retention beats Conversion
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Every free product depends on repeat purchase
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Nobody buys on the first engagement
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High long-term retention provides more opportunity to convert
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Optimizing near-term conversion has proved to be less effective

6. Why repeat purchase matters
One-time purchase Repeat purchases
Users
Free to paying conversion rate
Single purchase value
Monthly repeat customers
Six-month sales
Revenue increase
100 000
100 000
5%
5%
2.00 €
2.00 €
0%
10 %
10 000 €
15 000 €
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50 %

7. It’s really hard to predict retention
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Except: an engaged user is more likely to return
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How many return one day after
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What’s happening when people return
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1-7-30 day retention curve
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Typically, 30 days is enough to form a habit.
Are the next 30 days similar to the first 30?

8. Re-investing for growth
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Design for repeat purchase
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Optimize for high engagement and retention
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Learn to recognize who will engage and retain
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Re-invest revenues to acquire more people likely to engage
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Paid user acquisition
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Viral spread, eg sharing
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Community development
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Further product development

9. Do not measure averages
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Practically all human behavior is biased towards extremes
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Standard normal distribution applies well to physical measures, not behavior
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This is the same power law curve as in the Long Tail
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Average is driven by the outliers, but doesn’t represent them
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What’s the behavior of the highest and lowest 25%, ie, Interquartile range

10. Retention is not the same as Churn
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Churn = the % of users lost over a period, on average
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Retention = the % of people of a certain cohort age who stay active
Not unreasonable to expect that Retention = 1 - Churn. Why is this wrong?
An active user is more likely to stay active than the average!

11. Churn vs retention, visualized

12. What should I measure?
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Everything. Oh, is that not helpful?
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Specific events during the experience
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Frequency and periodicity of repeat events
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As wide a set of different events as is feasible to gather
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Clicks and other UI use is rarely meaningful, outside of UI optimization
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What is the product meant to do?

13. How should I measure?
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Event streams are semi-structured log files
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Time, identifier, event, event-specific data, context data
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Aim for dozens, if not hundreds of events per visit
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Expect to combine multiple sources of data and build context
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“Big” data: 20 MB per 1000 users per day
“Complex” data: event and source type specific processing logic
Timely feedback loops need near-realtime processes
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Streaming data infrastructures

14. Okay, I’ve measured. What now?
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Dashboards are Step 0. “What’s happening?”
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Ability to drill down: “Who, where, why is that happening?”
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Act on findings
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Customer contact
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Product changes
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Feedback loops: “Did anything change?”
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Testing: A/B, multivariate, pilot groups
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Segmented and personalized experience

15. Recap
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Data is essential in managing complex products
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Understand key principles. Avoid averages.
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You’re in the driver seat. Even real-time data is mostly a backwards mirror.
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Use data to validate assumptions, confirm results, (dis)prove hypotheses
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Data does not replace a product vision or design intent
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Data Science is a specialist skill

16. Thank you!
Osma Ahvenlampi
osma@metrify.io
www.metrify.io
twitter.com/metrify