Powering a Player-First Culture with Massive Gameplay Data€¦ · Engagement Who to target?...
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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
Transcript of Powering a Player-First Culture with Massive Gameplay Data€¦ · Engagement Who to target?...
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
Computer Science PhD
Distributed/Fault-
Tolerant Systems
Comparison Shopping
Startup
Ask.com
Search Engine Core
Web/News Search
Components
VP Data Systems
Electronic Arts
Digital Platform, Data
Science & Engineering
New Large Scale Data
Platform
Unlock Value of EA’s
Rich Gameplay Data
Outline
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• EA and Games
Why Data Matters
• Large Scale Data Platform
Design and Architecture for Gamer & GamePlay Data
• Data in Action
Examples of Data Usage
EA Overview
• Rich history of games, founded 1982
Current Strategic Goals:
• Digital Transformation
• Player First Culture
• Dozens of games, multiple platforms: console, pc, mobile• 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• Game updates, new features, new games
Live Services• Game operations
• Gameplay optimization
• Fraud
Marketing• Player acquisition, re-engagement
• Cross Promotions
• Advertisement
Customer Service• Player Facing Issues with Game
Executive Decisions
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Advert Push NoteEmail Personalized
features
In-game
Banner
Acquisition
CE
Customer
Experience
Example Player Journey through EA Ecosystem
Digital Platform: Data Science & Engineering
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Core Tech Principles
Leverage Open-Source• Join the community and ride its progress – requires investment in talent
Embrace the Benefits of the Cloud• 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• One platform for all EA games
• Standards with flexibility to support variations of use
Invest in “Crown Jewel IP” Data Components• Data Science, Algorithms, Data Layer Tools
• Smart build vs buy decisions
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Data Sources
Access & Applications
Storage & Processing
Reporting
& BI Tools
Game
Analytics
Subscription
API
Game
Servers
Marketing,
Ads, …
External Sources
And More…
Access Layer
Player
360
Segmentation
Manager
Engagement
ManagerExperimentation
Applications
Lightning (Streaming Ingestion & Processing) Tide (Batch Ingestion)
Capture &Ingestion
Access
API
Live
Viewer
Bug
Sentry
River
(Capture layer)
Shark
(Processing)Ocean
(Hadoop storage)
Pearl
(RDBMS)Black Pearl
(RDBMS)
Pond
(Hive)
Data Platform Architecture
Surf
(Data Science)
Platform
Services
Data Capture & Ingestion
Data Sources
• Client Telemetry (mobile, console, pc)
• Server Telemetry
• EA Internal Services• 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• 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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Game Client
Recent Gameplay
Historical Profile
Predicted
Churn Risk
(0% – 100%)
Mapping to
Chosen
Difficulty
Recommended
Levers to Pull
Targeting Recommendation
Churn Risk
How Dynamic Experience Works
Managing Player Relationships
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Provide the
right value
Data
Science
What to show
them?
Optimization
How to reach
them?
Engagement
Who to
target?
Segmentation
A set of tools to curate the player journey
through differentiating and improving the
player engagement
EA Games
A self-serve tool which enables granular
targeting of EA players.
Segmentation
Manage and deliver targeted messages to players
in-game, out of game, across the EA network
Engagement
Identify the best placement to engage, track, and
test messages to our players
Optimization
Optimize the Player First experience
using Data Science
Data Science
Player Relationship Management – Application Components
• Player Profile
• Segmentation via Indexing of key attributes, leverage Lucene• Examples: demographics, game ownership, play time, etc
• within seconds
• Run-time Decisioning Engine
• Communication Channels• Email, PushNote, in-game msg
• Campaign management
• Recommendations, optimizations
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Anomaly Detection and Reacting to Issues
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