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 Search Engine Core Digital Platform, Data Distributed/Fault- Web/News Search Science & Engineering Tolerant Systems Components New Large Scale Data Comparison Shopping VP Data Systems Platform Startup 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
3 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
4 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
5 Example Player Journey through EA Ecosystem
Acquisition Email Push Note In-game Advert Personalized Banner features
CE
Customer Experience Digital Platform: Data Science & Engineering
7 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
8 Data Platform Architecture
External Sources Data Game Platform Marketing, Servers Services Ads, … Sources And More…
River (Capture layer) Capture & Lightning (Streaming Ingestion & Processing) Tide (Batch Ingestion) Ingestion
Shark (Processing) Ocean (Hadoop storage) Storage & Processing Surf Black Pearl Pond Pearl (Data Science) (RDBMS) (Hive) (RDBMS) Access Layer Reporting Bug Game Live Subscription Access & BI Tools Sentry Analytics Viewer API API Access & Applications Applications Player Segmentation Engagement Experimentation 360 Manager Manager 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
10 Streaming and Lambda Architecture
Tech Stack • Kafka • distributed pub/sub messaging • Storm • stream event processing
11 Storage & Processing Engine
Storage: multi-tier approach • HDFS • Cloud Storage • Archive/Backup Tradeoff: cost vs performance
Processing Engine • Apache Hadoop Stack: Hive, Oozie
12 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
13 Data in Action: Examples
14 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
15 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
16 How Dynamic Experience Works
Targeting Recommendation
Predicted Churn Risk Mapping to Recent Gameplay Churn Risk Chosen (0% – 100%) Difficulty Game Client
Recommended Levers to Pull Historical Profile
17 Managing Player Relationships
A set of tools to curate the player journey Who to How to reach through differentiating and improving the target? them? player engagement Segmentation Segmentation Engagement A self-serve tool which enables granular targeting of EA players.
Engagement EA Games Manage and deliver targeted messages to players in-game, out of game, across the EA network Provide the What to show Optimization right value them? Identify the best placement to engage, track, and test messages to our players Data Optimization Science Data Science Optimize the Player First experience using Data Science
18 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
19 Anomaly Detection and Reacting to Issues
20 We’re Hiring! Data Scientists & Engineers Contact me! Navid Aghdaie Thank You! [email protected]
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