the game life-cycle and game analytics: what metrics ...€¦ · • game with better retention has...

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

2 Copyright HoneyTracks

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


The never-ending quest for the most important 5

metrics …



The never-ending quest for the most important 5 metrics

…

… is indeed a never-ending quest

.

.

.



… 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


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


Double the virality? Half the churn-rate?

What would you rather have?

Game Life-Cycle & Metrics Why retention comes first


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


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


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


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


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


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


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


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


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


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)


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)


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


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


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)


Want to see HoneyTracks in action?

Check out:

www.honeytracks.com @HoneyTracks

Mark Gazecki [email protected]

Contact information


the game life-cycle and game analytics: what metrics ...€¦ · • game with better retention has...

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