Mark's HoneyTracks presentation at Casual Connect 2012 in Hamburg on Feb 9th: What metrics / KPIs should you focus on along the different life cylce stages of your game.

1. The Game Life-Cycle and Game Analytics: What metrics matter when?
Casual Connect Hamburg 2012
Mark Gazecki (Chairman)

2. Introduction
HoneyTracks: Web-based game analytics solution
Deep analytical capability For all types of games Real-time / near real-time
Cohort analysis, funnels, Social games, browser-games,
data-filtering client games, mobile games
Custom metrics & funnels Easy-to-use graphical For everyone in the company
interface Information at everyone’s finger-
Avoiding data-graveyards tips: Game design, product mgmt,
(happens if people can’t use it) marketing, management, …
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3. Game Life-Cycle & Metrics
The 5 most important metrics
The never-ending quest
for the most important 5
metrics …
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4. 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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5. 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 What is important changes over the life-time of a
game must be considered. This has an implication game. This must be reflected in the metrics / KPIs
for the metrics you want to monitor.
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6. 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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7. Game Life-Cycle & Metrics
Virality vs retention
What would
you rather have?
Double the virality? Half the churn-rate?
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8. Game Life-Cycle & Metrics
Why retention comes first
Number of active users (conceptual)
3000 Assumptions Viral game Ret. game
Viral invites /
2500 user
2.5 1.25
Viral game
2000 Churn-rate 80% 40%
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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9. Game Life-Cycle & Metrics
Game life-cycle KPI framework
Game Life-Cycle (time / age of game)
User acquisition
Retention Monetization
Bring initial users Virality
into the game
(x-promotion,
“limited launch”)
Engagement Acquisition & Monetization
metrics virality metrics 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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10. 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
Tutorial steps funnel game)
• A/B-test user funnels
Drop-off rates (by level) • Optimize user drop-off events (make it less
difficult, more “funner”, …)
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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11. 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 • A/B-test different creatives
(cohort analysis)
• A/B-test different targeting (demographics,
geographies)
Metrics by demographics (cohort analysis)
• Monitor PLTV > PAC (for channel cohorts,
demographies etc)
Metrics by geography (cohort analysis)
Metrics by user source (e.g. player life-
time value) (ads, viral, x-promotion)
Start tracking monetization
metrics by user cohorts early on
(channels, demography, …)
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12. 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 32
Marketing Channel
1 2 3 4 5 6 7 8 9 10 11 32 33
Marketing Channel Comparing payouts to
„revenues“ shows that
Channel 1 has more „lost
revenue“, i.e. issues in the
We could improve the game: payment process
• 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 Marketing Channel 32
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13. Game Life-Cycle & Metrics
Virality metrics: What to start with
Virality Metrics
k-factor
Number of sent invites / DAU
• A/B-test content for viral message (how
should buttons look, images, etc)
Acceptance rate (by type of invite) • 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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14. Game Life-Cycle & Metrics
Monetization metrics: What to start with
Monetization Metrics
ARPU
ARPPU • A/B-test alternatives to improve first-time
buyer conversion (e.g. specials, variants of that
particular virtual good)
Payment conversion rate
• Optimize user-flow towards first purchase
trigger (-> get more users there)
Avg. transaction value • 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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15. Custom Metrics
Game life-cycle KPI framework: Introducing custom-metrics
User acquisition
Retention Monetization
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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16. Custom Metrics
Drill-down capability & custom metrics to derive actionable insight
Observe slight decrease
“Peeling in aggregate ARPU
the onion” in month of February
Payment conversion rate ARPPU
is decreasing remains constant
Payment conversion Payment conversion
for existing users stays for the user cohort acquired
constant in January is very low
Users acquired in January The pricing for a virtual
Mix of users in January
from marketing channel good, which typically was
shifted towards countries
“SuperDuperAds” have a the first virtual good
with generally lower
significantly lower purchased by users, was
conversion rates
conversion rate changed
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17. Game Life-Cycle & Metrics
Example: ARPU cohort analysis
Screenshot: ARPU cohort analysis
Aggregate ARPU
is 2 Euro
Monthly cohorts show
... and we see that that ARPU actually
ARPU improved from becomes 4 Euro!
April to May cohort
• 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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18. Game Analytics Examples
„Peel the onion“: Payment conversion (1)
Screenshot: Revenue analysis by level
Pretty effective at
Majority of revenues monetizing advanced
achieved in levels 20-30 users ...
0 10 20 30 40 50
0 10 20 30 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 10 20 30 40 50
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19. 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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20. Game Analytics Examples
„Peel the onion“: Whales Analysis
What is her
profile
Who are my
whales?
What, when and how
much of each item
works best for her?
What payment
We could improve the game does she use
• 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 Different colors indicate different feature/
for other whales item types - “mouse over “shows details
• Optimize payment options
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21. 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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22. Contact information
Want to see HoneyTracks in action?
Check out:
www.honeytracks.com
@HoneyTracks
Mark Gazecki
mg@honeytracks.com
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