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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
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Deep analytical capability Cohort analysis, funnels, A/B-testing
For all types of games Social games, browser-games, client games, mobile games
Real-time
Custom metrics & funnels Easy-to-use graphical interface Avoiding data-graveyards (happens if people can’t use it)
For everyone in the company Information at everyone’s finger-tips: Game design, product mgmt, marketing, management, …
Game Life-Cycle & Metrics The 5 most important metrics
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The never-ending quest for the most important 5
metrics …
Game Life-Cycle & Metrics The 5 most important metrics
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The never-ending quest for the most important 5 metrics
…
… is indeed a never-ending quest
.
.
.
Game Life-Cycle & Metrics The 5 most important metrics
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… there is no such thing
as the universal 5 most important metrics
Games are unique & different
To generate actionable insight differences in each game must be considered. This has an implication for the metrics you want to monitor.
Games have a life-cycle
What is important changes over the life-time of a game. This must be reflected in the metrics / KPIs
Game Life-Cycle & Metrics Moore‘s lifecycle adoption model applied to games
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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
Game Life-Cycle & Metrics Your‘re launching your game: Virality vs retention
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Double the virality? Half the churn-rate?
What would you rather have?
Game Life-Cycle & Metrics Why retention comes first
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0
500
1000
1500
2000
2500
3000
Month 1
Month 2
Month 3
Month 4
Month 5
Month 6
Month 7
Month 8
Month 9
Number of active users (conceptual)
Viral game
Game with better retention
• 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
Assumptions Viral game
Viral invites / user
Churn-rate
Ret. game
2.5 1.25
80% 40%
Game Life-Cycle & Metrics Game life-cycle KPI framework
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Retention
User acquisition
Virality
Monetization
Engagement metrics
Acquisition & virality metrics
Monetization metrics
Game Life-Cycle (time / age of game)
• 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!!!
Bring initial users into the game (x-promotion,
“limited launch”)
Game Life-Cycle & Metrics Retention metrics: What to start with
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Retention / Engagement Metrics
1-7 day retention
Tutorial steps funnel
Churn-rate (monthly)
Drop-off rates (by level)
Visits / DAU
Session times
• Optimize tutorial (to get users effectively into the game)
• A/B-test user funnels • Optimize user drop-off events (make it less
difficult, “more fun”, …)
• Give user more / less stuff to do / more energy (-> session length. engagement)
• Track feature-usages (also for mid- / end-game) • A/B-test game mechanics (esp. mid- / end-game)
1 / monthly churn-rate =
Player lifetime (in months)
Game Life-Cycle & Metrics Acquisition metrics: What to start with
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Acquisition Metrics
Conversion rates (CTR)
User acquisition cost (CPC, CPI / PAC)
Metrics by user source (e.g. player life-time value) (ads, viral, x-promotion)
Metrics by marketing channel / ad (cohort analysis)
Metrics by demographics (cohort analysis)
Metrics by geography (cohort analysis)
• Test different marketing channels • A/B-test different creatives
• A/B-test different targeting (demographics, geographies)
• Monitor PLTV > PAC (for channel cohorts, demographies etc)
Start tracking monetization metrics by user cohorts early on
(channels, demography, …)
Game Life-Cycle & Metrics Example: Marketing channels
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Marketing Channel
1 2 3 4 5 6 7 8 9 10 11 32 33
1 32
1 32
Marketing Channel
Marketing Channel
Segmenting users by marketing channel ...
... shows that Channel 1 has 50% of Channel 32
revenues despite having 2.5x in DAU
Comparing payouts to „revenues“ shows that
Channel 1 has more „lost revenue“, i.e. issues in the
payment process
Screenshot: Channel profitability
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
Game Life-Cycle & Metrics Virality metrics: What to start with
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Virality Metrics
k-factor
Number of sent invites / DAU
Acceptance rate (by type of invite)
% of virally acquired users (last 30 days cohort)
Number of viral users by viral source
• 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
Game Life-Cycle & Metrics Monetization metrics: What to start with
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Monetization Metrics
ARPU
ARPPU
Paying user cohort (by marketing channel, by geography)
Payment conversion rate
Avg. transaction value
First purchase trigger
• 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)
• A/B-test pricing
Player life-time value (PLTV)
Custom Metrics Game life-cycle KPI framework: Introducing custom-metrics
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Retention
User acquisition
Virality
Monetization
Standard 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
Custom metrics
Custom Metrics Drill-down capability & custom metrics to derive actionable insight
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ARPPU remains constant
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
Observe slight decrease in aggregate ARPU
in month of July
The pricing for a virtual good, which typically was
the first virtual good purchased by users, was
changed
“Peeling the onion”
Game Life-Cycle & Metrics Example: ARPU cohort analysis
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Screenshot: ARPU cohort analysis
Aggregate ARPU is 2 Euro
Monthly cohorts show that ARPU actually becomes 4 Euro!
... and we see that ARPU improved from 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
Game Analytics Examples „Peel the onion“: Payment conversion (1)
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Majority of revenues achieved in levels 20-30
0 10 20 30 40 0 10 20 30 40 50
... but what about users in earlier levels?
0 10 20 30 40 50
What are virtual goods that are useful at earlier
levels?
Pretty effective at monetizing advanced
users ...
Can we push users into making purchases
earlier?
Screenshot: Revenue analysis by level
Game Analytics Examples „Peel the onion“: Payment conversion (2)
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We could improve the game • Offering „food“ specials to users at lower
levels
• Try lower prices for food to generate more first time buyers
... even though „food“ doesn‘t play a major role
in revenues
At lower levels „food“ is being purchased relatively
higher
... so this may be the virtual good, which
converts users into „first time buyers“
Game Analytics Examples „Peel the onion“: Whales Analysis
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What payment does she use
What is her profile
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
Who are my whales?
What, when and how much of each item works best for her?
Different colors indicate different feature/item types - “mouse over “shows details
Game Analytics & Game Life-Cycle How to approach it right
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Start with standard metrics. Then move to custom metrics to generate actionable insight
Start with retention metrics. Then move to user acquisition-, virality-, and monetization metrics.
„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)
Game Analytics & Game Life-Cycle Read more! Casual Connect Magazine (summer 2012)
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Want to see HoneyTracks in action?
Check out:
www.honeytracks.com @HoneyTracks
Mark Gazecki [email protected]
Contact information
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