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What can we learn from
game analytics?

Hello
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Osma Ahvenlampi, founder, Metrify.io
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○

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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

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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○

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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

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

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

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 €

-

50 %

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?

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

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

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!

Churn vs retention, visualized

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?

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

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

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

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

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