Game analytics can provide valuable insights into user acquisition, retention, engagement, and monetization. Key metrics include the acquisition cost of new users, how many users are retained over time, how engaged users are with the game, and how often engagement leads to revenue. Retention is particularly important as it provides more opportunities for conversion and repeat purchases over time. Analytics can help optimize the game experience and identify which users are most likely to engage and be retained by focusing on specific in-game events and behaviors rather than averages. The data collected should then be used to test changes, provide personalized experiences, and continuously improve the game.

1. What can we learn from
game analytics?

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

3. Analytics changed games forever
●
Games used to be almost completely unmonitored and analyzed once
released to the market
○
●
That, is, analysis done on them was desktop reverse engineering
Today, they’re among the most comprehensively analyzed products
○
○
●
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
●
Acquisition: where, how & at what cost can new users be found
●
Retention: how many stay over a period of time
●
Engagement: how much time do people consume
●
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
●
Every free product depends on repeat purchase
●
Nobody buys on the first engagement
●
High long-term retention provides more opportunity to convert
●
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 €
-
50 %

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

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

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

10. Retention is not the same as Churn
●
Churn = the % of users lost over a period, on average
●
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?
●
Everything. Oh, is that not helpful?
●
Specific events during the experience
●
Frequency and periodicity of repeat events
●
As wide a set of different events as is feasible to gather
●
Clicks and other UI use is rarely meaningful, outside of UI optimization
●
What is the product meant to do?

13. How should I measure?
●
Event streams are semi-structured log files
●
Time, identifier, event, event-specific data, context data
●
Aim for dozens, if not hundreds of events per visit
○
●
Expect to combine multiple sources of data and build context
○
●
“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
○
Streaming data infrastructures

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

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

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