1. 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

2. 2 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

3. Outline 3 EA and Games  Why Data Matters Large Scale Data Platform  Design and Architecture for Gamer & GamePlay Data Data in Action  Examples of Data Usage

4. 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

5. 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

6. AdvertPush Note Email Personalized features In-game Banner Acquisition CE Customer Experience Example Player Journey through EA Ecosystem

7. Digital Platform: Data Science & Engineering 7

8. 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

9. 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 Manager Experimentation Applications Lightning (Streaming Ingestion & Processing) Tide (Batch Ingestion) Capture & Ingestion Access API Live Viewer Bug Sentry River (Capture layer) Shark (Processing) Ocean (Hadoop storage) Data Platform Architecture Platform Services

10. 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 1 st Party (e.g. sales data from xbox, playstation, android, ios) 3 rd Party (e.g. acquisition marketing, ads) EA web sites traffic Challenges: Definition and Enforcement of taxonomy standards Silos and Duplication 10

11. Streaming and Lambda Architecture Tech Stack Kafka distributed pub/sub messaging Storm stream event processing 11

12. Storage & Processing Engine Storage: multi-tier approach HDFS Cloud Storage Archive/Backup Tradeoff: cost vs performance Processing Engine Apache Hadoop Stack: Hive, Oozie 12

13. 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

14. Data in Action: Examples 14

15. 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

16. 16 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

17. 17 Game Client Recent Gameplay Historical Profile Predicted Churn Risk (0% – 100%) Mapping to Chosen Difficulty Recommended Levers to Pull TargetingRecommendation Churn Risk How Dynamic Experience Works

18. Managing Player Relationships 18 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

19. 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

20. Anomaly Detection and Reacting to Issues 20

21. Thank You! 21 We’re Hiring! Data Scientists & Engineers Contact me! Navid Aghdaie naghdaie@ea.com