What can we learn from game analytics?

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

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

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

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

Why repeat purchase matters

One-time purchase Repeat purchases

Users 100 000     100 000    

Free to paying conversion rate 5 % 5 %

Single purchase value 2.00 € 2.00 €

Monthly repeat customers 0 % 10 %

Six-month sales 10 000 € 15 000 €

Revenue increase - 50 %

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?

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

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

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!

Churn vs retention, visualized

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?

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

○ “Big” data: 20 MB per 1000 users per day

● Expect to combine multiple sources of data and build context

○ “Complex” data: event and source type specific processing logic

● Timely feedback loops need near-realtime processes

○ Streaming data infrastructures

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

● 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

Recap

Thank you!

Osma Ahvenlampi

osma@metrify.io

www.metrify.io

twitter.com/metrify