Chris Wright: Games Analytics

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Copyright GamesAnalytics ©2011

Analytics 101 – A crash course on why you should care

October 2011

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2

Who am I?

16 years in the games industry

Involved in over 10 console games and over 100 mobile games

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Analytics, boring?

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How Zynga changed the world

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Copyright GamesAnalytics ©2011Analytics Tips For Games

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Focus on the player

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Focus On The Player

Focusing on the Player

Eva Whitlow

Have added some more types of players to show how diverse players are today

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Focus On The Player

• Build a player centric view of game design

• Model your expected user base

• Understand how different players interact with the game

• Customise the game based on player behaviour

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Focus On The Player

• Aim to make the players enjoy the gameplay – in the different ways

they play – sociable, completers, explorers

• Length of gameplay is generally a good indication of monetisation

• Analytics allows the game to be tailored to individual types of players

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Copyright GamesAnalytics ©2011Co

llect

the

rig

ht

Dat

a

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Collect The Right Data

• Understand the information you want to analyse

• Focus on player level, not game level information

• Identify significant events in the game

• Build good data integrity

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Collect The Right Data

Good Events Bad Events

Player Progress Browser & Device Type

Player Tasks Bugs and Crashes

Items Bought & Sold Movement

Items Gifted Buttons Clicked

Levels Page Views

Missions

Friends

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Understand the metrics

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Understand The Metrics

• Metrics generally means dashboards

• This provides historic information

• It tells you the health of your game

• Use metrics to identify the areas of the

game that need focus

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Understand The Metrics• Retention

• Engagement

• Life Time Value

• ARPU / ARPPU

• Whales

• Time to First & Second Payment

• DAU / MAU

• Player Behaviour

• Demographics

• Game specific metrics

• Virality

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Segment Player Behaviour

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Segment Player Behaviour


Revenue Potential

Vira

lity

Pot

entia

l

31%0.89%22%$1.75

%Volume%Paying7Day Ret

CAC

25%1.30%26%$2.21

5%0.199%$2.3

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14%0.9721%$1.9

4

Early Enthusiasts

Confident Completers

Social Involver

Sporadic Semi Engaged

Losing Momentum

Need Guidance

Borderline Incompetent

6%0.3457%$4.40

12%0.8659%$3.5

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7%0.5536%$0.75

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Copyright GamesAnalytics ©2011 So

cial

in

tera

ctio

n

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Social Interaction

• Connectedness

• Centrality

• Cohesion

• Reciprocity

• Social Influence

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Social Interaction


• Identify player ‘bridges’

• Isolated players

• Gaps and holes, leading to group fragmentation

• Reward highly influential players

• Manage cohesion, caused by influential players

• Reconnect isolated players

• Build bridges and connect sub networks

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Copyright GamesAnalytics ©2011Identify player value

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Identify Player Value• Time to first payment (2-4 weeks drive high LTV)• Payment patterns (regular, increasing, decreasing)• Triggers for first spend (why now?)• Time lag to second spend (be quick)• Reasons for reactivation (paying players stick around)

• All actionable to raise LTV• Overlay profiles to refine targets

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Copyright GamesAnalytics ©2011Pattern analysis

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Pattern Analysis


1st Event 2nd Event 3rd Event

61%

12%

9% Challenge Start

Gifted item

Visited Home

Invite Neighbour

Bought Item

Use event order to predict andencourage next action

Intervention here

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Pattern Analysis

• Pattern analysis is a powerful technique

• Using it allows behaviours to be tracked and identified

• This can be used to react to next best option

• This can also be used to identify actions that precede abandonment

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Copyright GamesAnalytics ©2011Predictive Modelling

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Predictive Modeling

• Ability to predict player behaviour

• Identify players likely to undertake an action if encouraged

• Provide the means to deliver a marketing intervention that is:

• Timely

• Personal

• Appropriate

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Simple Multivariate Predictive Model


1 2 3 4 5 6 7 80

0.05

0.1

0.15

0.2

0.25

Variable Contribution

#Sessions 11+

Gifted Item

Total Stamina 5000+

Highest Level 10+

Wounded Giant

Accepted Invite

Friend Count 10+

Run Away

Likelihood N Y

Least 99.5% 0.5%

2 98.9% 1.1%

3 99.5% 0.5%

4 99.5% 0.5%

5 98.4% 1.6%

6 95.8% 4.2%

7 95.3% 4.7%

8 93.6% 6.4%

9 85.2% 14.8%

Most 62.2% 37.8%

Total 92.8% 7.2%

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Copyright GamesAnalytics ©2011 Act

ion

able

re

sults

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Actionable Results

• The key to Analytics is to provide the tools to improve the game

• This can be:

• Improve Gameplay

• Increase Revenue

• Reduce Abandonment

• Increase Retention

• Reward Loyalty

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Actionable Results

• Analytics provides the means to identify these traits

• To group players into manageable segments

• To predict their future behaviour

• To intervene to change behaviours and move the graph

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Actionable Results

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Copyright GamesAnalytics ©2011Analytics in Games

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The Industry

• Analytics is becoming a key skill in game development

• Zynga has a 60 person Analytics team

• Analytics allows game design to understand the player

• Games are increasingly becoming data driven

• Games that adapt to the player is the future

(the one size fits all is dead)

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Any Questions?

Entertainment & Humor

Chris Wright: Games Analytics