Pursuing Excellence through Analytics: How EA Has Used Analytics To Up It’s Game Zachery Anderson VP Global Analytics & Insights SAFE HARBOR STATEMENT Some statements set forth in this document contain forward-looking statements that are subject to change. Statements including words such as “anticipate,” "believe,” “estimate” or “expect” and statements in the future tense are forward-looking statements. These forward-looking statements are preliminary estimates and expectations based on current information and are subject to business and economic risks and uncertainties that could cause actual events or actual future results to differ materially from the expectations set forth in the forward-looking statements. Some of the factors which could cause the Company’s results to differ materially from its expectations include the following: sales of the Company’s titles; the Company’s ability to manage expenses; the competition in the interactive entertainment industry; the effectiveness of the Company’s sales and marketing programs; timely development and release of the Company’s products; the Company’s ability to realize the anticipated benefits of acquisitions; the consumer demand for, and the availability of an adequate supply of console hardware units; the Company’s ability to predict consumer preferences among competing platforms; the Company’s ability to service and support digital product offerings, including managing online security; general economic conditions; and other factors described in the Company’s Quarterly Report on Form 10-Q for the fiscal quarter ended December 31, 2015. These forward-looking statements are valid only as of the date on which they are made. Electronic Arts assumes no obligation and does not intend to update these forward-looking statements. Please review our risk factors on Form 10-Q filed with the SEC. Electronic Arts (EA) • Founded in 1982 • Games, content and live services on console, PC, mobile and tablet • 1.5 Billion registered players in over 200 countries • Over 2 Billion downloads of EA mobile games • #1 Publisher on Sony PlayStation and Microsoft XBOX • FY17 Revenue Target $4.75 Billion All Financial Data is Non-GAAP except Free Cash Flow Free Cash Flow Calculated as Operating Cash Flow less Capex A Corporate Journey Timeline Global FIFA Ultimate Battlefield 3 Worst Mktg 22% CEO: Andrew EA Digital Rev Core FIFA #1 financial crisis Team Origin Company of Revenue Wilson Access ~50% Player Battlefield 1 #2 In America Metrics Western World HD 2008 2009 2011 2012 2013 2014 2015 Today EA Focus player experience sell-through sell-in ~90$ NASDAQ ~50$ ~20$ ~11$ Marketing Lots of Play = Lots of Data ~5 Petabytes Game Telemetry, registrations, marketing events, web traffic Growing at 25 Terabytes daily We Are An Entertainment Business Share of lifetime sales volume 80% 75% Drop Sales Volume Sales -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Release Weeks of launch …..And Maybe The Weeks Beforw Share of lifetime sales volume 80% 25% Sales Volume Sales -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Release Weeks of launch Marketing Focused High Sales Periods Launch campaign Holiday campaign Sales Volume Sales -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Release Weeks of launch Who are the players? New to EA = First EA Game New to Franchise = First time playing Madden Veteran = Has previously played Madden 100% Launch 90% 80% 70% 60% Holidays 50% # ofPlayers # % ofPlayers % 40% 30% Black Friday 20% 10% 0% 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Weeks since Launch Weeks since Launch PLAYER ANALYTICS We Are Spending at The Wrong Time AND FORECASTING General Reasons for purchase: $5M Media 1. Previous game 2. Genre interest 3. Friends 12M Vets reason to $80M Media purchase Hardline: 1. I always 3M 13M purchase (65% played 10+ session days) 2. Liked the Beta 6M (72% vets played 10+ session days) 3. Liked the Theme Veterans New to Franchise Customer Lifetime Value In-Sample Fit Given an initial entry into a game, how many base-games can 6000 2011 Cohort (10k users) we reasonably expect a user to purchase?* 5000 4000 3000 Actual Model 2000 No. of people 5 1000 0 years • Y Number of base game purchases 0 1 2 3 4 5 • More than X% users made 2 or more No. of Repeat Transactions repeat purchases Cumulative Tracking • X% of users purchased every installment 15000 2011 Cohort (10k users) • X% of users only purchased one installment 10000 Actual 5000 10 Model 0 years • Y Number of base game purchases Cum. of # repeattransactions 2010 2012 2014 2016 2018 2020 2022 (nearly X% growth from 5 year) Fit • X% of users estimated to purchase Absolute Average Error: 2.1% every installment MAPE: .038281 IT’S A SUBSCRIPTION SERVICE Efficiency & Effectiveness M&A as % of Revenue 22% to 16% Product Monitoring Activity Monitoring Daily Active Users 8/26 9/2 9/9 9/16 9/23 9/30 10/7 10/14 10/21 10/28 11/4 11/11 Daily Active Users Dailyand ChurnActive 11/18 11/25 12/2 12/9 12/16 12/23 12/30 1/6 1/13 1/20 1/27 Calendar Date 2/3 2/10 2/17 2/24 3/3 3/10 3/17 3/24 3/31 4/7 4/14 4/21 4/28 5/5 5/12 5/19 5/26 6/2 6/9 6/16 6/23 6/30 7/7 7/14 7/21 7/28 # of Churners per Calendar Date Finding Play Patterns How Hard Should The Game Be? 35% Sweet Spot 30% Successful Unsuccessful 25% 20% Trend turns towards churning once 15% players start living too long 10% % of Player Type Base Type Player % of 5% 0% < 30 < 60 < 90 < 120 < 150 < 180 < 210 < 240 < 270 < 300 < 330 < 360 360 + Average Seconds Between Spawn and Death Recommendations Changing The Game Players are provided with different tile combinations depending on what segment the model places them within Identifying Unlikely to Reach Ten SD DANGER Differing Churn Prop NO DANGER Likely to Reach Ten SD Grey buttons are the tiles. Screens extracted from recommendation manager 19 Finding Balance Match Balance Distribution Conquest – BF4 100 Previous Game Multiplayer Matchmaking 80 60 “Fair” matches lead to a nearly 10% higher 40 likelihood of playing another round 20 0 % of balanced games in Battlefield 4: 40% Imbalance 10020% Conquest – BF1 18% Current Game 8016% 14% 6012% 10% Frequency 408% 6% 204% 2% % of balanced games in Battlefield 1: 80-85% 0%0 -1 -0,5 0 0,5 1 Imbalance Imbalance NPS Evolution 70 60 60 60 52 Title 3 Title 3 50 Wave 2 Launch Title 3 40 Holiday 30 24 Margin of error per wave: approx. +/-2 points (e.g. Launch range 58-62, Holiday range 50-54) 20 14 Title 2 12 Launch 10 10 Title 1 Title 1 Title 2 Launch Holiday 0 Holiday THE COMPANY Find the Key Decision Makers Producer Analyst Build an Analytics Habit The Cue that starts the habit. A weekly meeting to review metrics The Habit Itself. Using and player behaviors player insights to make decisions. 1 REMINDER ROUTINE 2 REWARD 3 If the reward is positive, then you’ll have The benefit from doing the habit, desire to repeat the action. Game teams Core Player Metrics is goals and need to see success from using data to recognition. make decisions. Change the Measures Unique Session Average NPS Players Days Spend SCALE TIME MONEY LOYALTY Core Player Metrics Tell Player (Human) Stories Started playing EA BF3: played 9 Mass Effect 3: Madden 25: Dead Space: Crysis 3: BF Hardline: (session) days games in 2011 on PS3 9 days 7 days 11 days 2 days 25 days Entered EA through BF3 Purchased 6 games in 4 years Usually plays 10 days or less Most played game: BFHL – 25 days Upgraded to PS4 in 2015 Played on that day QUESTIONS .