The Game Life-Cycle and Game Analytics: What metrics matter when?
Data Science Day Berlin
Mark Gazecki (Chairman)
Introduction
HoneyTracks: Web-based game analytics solution
Deep analytical capability
For all types of games
Cohort analysis, funnels,
A/B-testing
Social games, browser-games,
client games, mobile games
Custom metrics & funnels
Easy-to-use graphical
interface
Avoiding data-graveyards
(happens if people can’t use it)
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Real-time
For everyone in the company
Information at everyone’s fingertips: Game design, product mgmt,
marketing, management, …
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Game Life-Cycle & Metrics
The 5 most important metrics
The never-ending quest
for the most important 5
metrics …
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Game Life-Cycle & Metrics
The 5 most important metrics
The never-ending quest
for the most important 5 metrics
…
.
.
.
…
is indeed a never-ending quest
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Game Life-Cycle & Metrics
The 5 most important metrics
…
there is no such thing
as the universal
5 most important metrics
Games are unique & different
Games have a life-cycle
To generate actionable insight differences in each
game must be considered. This has an implication
for the metrics you want to monitor.
What is important changes over the life-time of a
game. This must be reflected in the metrics / KPIs
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Game Life-Cycle & Metrics
Moore‘s lifecycle adoption model applied to games
Prototypical Technology Product Lifecycle (taken from “Crossing the Chasm”)
Growth
Maturity & Revenues
• Like any other technology-product, games have a product lifecycle (may be
more or less pronounced for certain game-types and individual games)
• First focus is on growth then on managing maturity and maximizing revenues
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Game Life-Cycle & Metrics
Your‘re launching your game: Virality vs retention
What would
you rather have?
Half the churn-rate?
Double the virality?
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Game Life-Cycle & Metrics
Why retention comes first
Number of active users (conceptual)
3000
Assumptions
Viral game
Ret. game
2500
Viral invites /
user
2.5
1.25
80%
40%
Viral game
Churn-rate
2000
1500
1000
Game with better retention
500
0
Month Month Month Month Month Month Month Month Month
1
2
3
4
5
6
7
8
9
• Game with better retention has higher number of average monthly users
• No retention – no sustainable growth – no hit
• … and since users tend to monetize better as they progress in the game,
higher retention lays the basis for strong monetization
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Game Life-Cycle & Metrics
Game life-cycle KPI framework
Game Life-Cycle (time / age of game)
User acquisition
Monetization
Retention
Bring initial users
into the game
(x-promotion,
“limited launch”)
Virality
Engagement
metrics
Acquisition &
virality metrics
Monetization
metrics
• Start out by making sure that “retention” is good enough with an initial flow of
users, i.e. not all users you acquire churn out immediately
• Then move onto optimizing “user acquisistion”, “virality”, and “monetization
• … but of course this is an additive view!!!
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Game Life-Cycle & Metrics
Retention metrics: What to start with
Retention / Engagement Metrics
1-7 day retention
• Optimize tutorial (to get users effectively into the
game)
Tutorial steps funnel
• A/B-test user funnels
Drop-off rates (by level)
• Optimize user drop-off events (make it less
difficult, “more fun”, …)
Visits / DAU
• Give user more / less stuff to do / more energy
(-> session length. engagement)
• Track feature-usages (also for mid- / end-game)
Session times
• A/B-test game mechanics (esp. mid- / end-game)
Churn-rate (monthly)
1 / monthly churn-rate
=
Player lifetime (in months)
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Game Life-Cycle & Metrics
Acquisition metrics: What to start with
Acquisition Metrics
Conversion rates (CTR)
User acquisition cost (CPC, CPI / PAC)
• Test different marketing channels
Metrics by marketing channel / ad
(cohort analysis)
Metrics by demographics (cohort analysis)
• A/B-test different creatives
• A/B-test different targeting (demographics,
geographies)
• Monitor PLTV > PAC (for channel cohorts,
demographies etc)
Metrics by geography (cohort analysis)
Metrics by user source (e.g. player lifetime value) (ads, viral, x-promotion)
Start tracking monetization
metrics by user cohorts early on
(channels, demography, …)
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Game Life-Cycle & Metrics
Example: Marketing channels
Screenshot: Channel profitability
... shows that Channel 1
has 50% of Channel 32
revenues despite
having 2.5x in DAU
Segmenting users by
marketing channel ...
1
1 2 3 4 5 6 7 8 9 10 11
32
Marketing Channel
32 33
Comparing payouts to
„revenues“ shows that
Channel 1 has more „lost
revenue“, i.e. issues in the
payment process
Marketing Channel
We could improve the game:
• Focus on user aquisition from ch32
• Double check payment type (SMS) and charge
backs in ch1
• Switch off certain payment methods at special times
1
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Marketing Channel
32
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Game Life-Cycle & Metrics
Virality metrics: What to start with
Virality Metrics
k-factor
Number of sent invites / DAU
Acceptance rate (by type of invite)
• A/B-test content for viral message (how
should buttons look, images, etc)
• A/B-test different viral triggers (in the game)
• A/B-test different acceptance mechanisms
% of virally acquired users
(last 30 days cohort)
Number of viral users by viral source
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Game Life-Cycle & Metrics
Monetization metrics: What to start with
Monetization Metrics
ARPU
ARPPU
Payment conversion rate
Avg. transaction value
• A/B-test alternatives to improve first-time
buyer conversion (e.g. specials, variants of that
particular virtual good)
• Optimize user-flow towards first purchase
trigger (-> get more users there)
• A/B-test different virtual goods & packages
• Optimize payment process (conversion steps)
First purchase trigger
• A/B-test pricing
Paying user cohort (by marketing
channel, by geography)
Player life-time value (PLTV)
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Custom Metrics
Game life-cycle KPI framework: Introducing custom-metrics
User acquisition
Monetization
Retention
Virality
Standard metrics
Custom metrics
• Standard metrics are great for detecting issues on a high level
• To derive actionable insight need to drill deeper and look at custom metrics
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Custom Metrics
Drill-down capability & custom metrics to derive actionable insight
Observe slight decrease
in aggregate ARPU
in month of July
“Peeling
the onion”
Payment conversion rate
is decreasing
Payment conversion
for existing users stays
constant
Payment conversion
for the user cohort acquired
in June is very low
Users acquired in June
from marketing channel
“SuperDuperAds” have a
significantly lower
conversion rate
Mix of users in June
shifted towards countries
with generally lower
conversion rates
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ARPPU
remains constant
The pricing for a virtual
good, which typically was
the first virtual good
purchased by users, was
changed
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Game Life-Cycle & Metrics
Example: ARPU cohort analysis
Screenshot: ARPU cohort analysis
Aggregate ARPU
is 2 Euro
... and we see that
ARPU improved from
April to May cohort
Monthly cohorts show
that ARPU actually
becomes 4 Euro!
• Aggregate numbers don‘t tell the
truth
• As a next step we would dig
deeper into the May-cohort to
understand why it generated
better ARPU
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Game Analytics Examples
„Peel the onion“: Payment conversion (1)
Screenshot: Revenue analysis by level
Pretty effective at
monetizing advanced
users ...
Majority of revenues
achieved in levels 20-30
0
0
10
20
30
10
20
30
40
50
40
... but what about users
in earlier levels?
Can we push users
into making purchases
earlier?
What are virtual goods
that are useful at earlier
levels?
0
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10
20
30
40
50
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Game Analytics Examples
„Peel the onion“: Payment conversion (2)
At lower levels „food“ is
being purchased relatively
higher
... so this may be the
virtual good, which
converts users into „first
time buyers“
... even though „food“
doesn‘t play a major role
in revenues
We could improve the game
• Offering „food“ specials to users at lower
levels
• Try lower prices for food to generate more
first time buyers
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Game Analytics Examples
„Peel the onion“: Whales Analysis
Who are my
whales?
What is her
profile
What, when and how
much of each item
works best for her?
What payment
does she use
We could improve the game
• See what works best for whales and offer
higher variety of same type
• Increase prices step-wise for new items
and monitor closely
• Try out special offers for items that work
for other whales
• Optimize payment options
Different colors indicate
different feature/item types “mouse over “shows details
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Game Analytics & Game Life-Cycle
How to approach it right
Start with retention metrics.
Then move to user acquisition-, virality-, and monetization metrics.
Start with standard metrics.
Then move to custom metrics to generate actionable insight
„Peel the onion“ to derive actionable insight
(cohort analysis etc)
Understand it is an ongoing effort, which involves multiple
functions / departments in your company (not all which are tech-people)
Make sure you have the right game analytics system
(it should support all of the above)
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Game Analytics & Game Life-Cycle
Read more! Casual Connect Magazine (summer 2012)
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Contact information
Want to see
HoneyTracks in action?
Check out:
www.honeytracks.com
@HoneyTracks
Mark Gazecki
[email protected]
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