Powering a Player-First Culture
with Massive Gameplay Data
A Sneak Peek into
Data and Electronic Arts
Navid Aghdaie, PhD
Sr. Director of Data Science & Engineering
Sep 2015
About Me
UCLA
Ask.com
Electronic Arts
Computer Science PhD
Distributed/FaultTolerant Systems
Comparison Shopping
Startup
Search Engine Core
Web/News Search
Components
VP Data Systems
Digital Platform, Data
Science & Engineering
New Large Scale Data
Platform
Unlock Value of EA’s
Rich Gameplay Data
Outline
• EA and Games
 Why Data Matters
• Large Scale Data Platform
 Design and Architecture for Gamer & GamePlay Data
• Data in Action
 Examples of Data Usage
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EA Overview
• Rich history of games, founded 1982
Current Strategic Goals:
• Digital Transformation
• Player First Culture
• Dozens of games, multiple platforms: console, pc, mobile
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Sports: FIFA, Madden, NHL, NBA
DICE: Battlefield, StarWars Battlefront
Bioware: Dragon Age, Mass Effect
Maxis: The Sims Franchise (Sims4), SimCity
Need for Speed, Bejeweled, Plants vs. Zombies, Simpsons Tapped Out, etc…
• 10s M players/day, across the world
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Data Usage at EA (Gameplay Data)
Game Design and Development
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Game updates, new features, new games
Live Services
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Game operations
Gameplay optimization
Fraud
Marketing
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Player acquisition, re-engagement
Cross Promotions
Advertisement
Customer Service
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Player Facing Issues with Game
Executive Decisions
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Example Player Journey through EA Ecosystem
Acquisition
Email
Push Note
In-game
Banner
Advert
Personalized
features
CE
Customer
Experience
Digital Platform: Data Science & Engineering
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Core Tech Principles
Leverage Open-Source
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Join the community and ride its progress – requires investment in talent
Embrace the Benefits of the Cloud
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Downward price trend
Lowers risk of volume/game success mispredictions
Build and spend only as needed
Avoid vendor lock-in
Build with Scalability, Extensibility, Reliability from the Start
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One platform for all EA games
Standards with flexibility to support variations of use
Invest in “Crown Jewel IP” Data Components
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Data Science, Algorithms, Data Layer Tools
Smart build vs buy decisions
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Data Platform Architecture
External Sources
Marketing,
Ads, …
Platform
Services
Game
Servers
Data
Sources
And More…
River
(Capture layer)
Tide (Batch Ingestion)
Lightning (Streaming Ingestion & Processing)
Capture &
Ingestion
Shark
(Processing)
Ocean
(Hadoop storage)
Surf
(Data Science)
Black Pearl
(RDBMS)
Storage &
Processing
Pearl
(RDBMS)
Pond
(Hive)
Access Layer
Reporting
& BI Tools
Applications
Player
360
Bug
Sentry
Game
Analytics
Segmentation
Manager
Live
Viewer
Subscription
API
Engagement
Manager
Access
API
Experimentation
Access &
Applications
Data Capture & Ingestion
Data Sources
• Client Telemetry (mobile, console, pc)
• Server Telemetry
• EA Internal Services
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e.g. online e-comemerce, micro txn, virtual goods purchase/trade, etc
1st Party (e.g. sales data from xbox, playstation, android, ios)
3rd Party (e.g. acquisition marketing, ads)
EA web sites traffic
Challenges:
• Definition and Enforcement of taxonomy standards
• Silos and Duplication
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Streaming and Lambda Architecture
Tech Stack
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Kafka
• distributed pub/sub messaging
• Storm
• stream event processing
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Storage & Processing Engine
Storage: multi-tier approach
• HDFS
• Cloud Storage
• Archive/Backup
Tradeoff: cost vs performance
Processing Engine
• Apache Hadoop Stack: Hive, Oozie
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Data Access & Applications
• Reporting & Dashboards
• Adhoc Analytics
• Hive (HQL)
• RDBMS (SQL)
• APIs, Data Subscription
• Closed-Loop Data Driven Online Applications
• Personalization/Targeting Systems
• Recommendation Engines
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Data in Action: Examples
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Dynamic Player Experience
Real-time recommendation engine
• Modify game configuration to optimize for targeted metrics
• Example:
Maximize retention by manipulating game difficulty according to user state
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Initial Configurations Dramatically Affect Win-Rates
Level: Deep Sea Creature
• Initial seed affects the starting board configuration
• # of orange, green, and purple pegs
• Potential locations of the pegs
• Win ratio ranges from 10-50% depending on the seed
• Effective knob for us to create a better experience
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How Dynamic Experience Works
Targeting
Recent Gameplay
Predicted
Churn Risk
(0% – 100%)
Recommendation
Churn Risk
Mapping to
Chosen
Difficulty
Game Client
Recommended
Levers to Pull
Historical Profile
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Managing Player Relationships
Who to
target?
How to reach
them?
Segmentation
Engagement
A set of tools to curate the player journey
through differentiating and improving the
player engagement
Segmentation
A self-serve tool which enables granular
targeting of EA players.
Engagement
EA Games
Provide the
right value
Data
Science
Manage and deliver targeted messages to players
in-game, out of game, across the EA network
What to show
them?
Optimization
Optimization
Identify the best placement to engage, track, and
test messages to our players
Data Science
Optimize the Player First experience
using Data Science
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Player Relationship Management – Application Components
• Player Profile
• Segmentation via Indexing of key attributes, leverage Lucene
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Examples: demographics, game ownership, play time, etc
• within seconds
• Run-time Decisioning Engine
• Communication Channels
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Email, PushNote, in-game msg
• Campaign management
• Recommendations, optimizations
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Anomaly Detection and Reacting to Issues
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We’re Hiring!
Thank You!
Data Scientists & Engineers
Contact me!
Navid Aghdaie
[email protected]
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