Inside the Obama Analytics Cave Andrew Claster, Deputy Chief Analytics Officer Obama for America W INNING K N OWLEDGE T M

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1 Inside the Obama Analytics Cave Andrew Claster, Deputy Chief Analytics Officer Obama for America W INNING K N OWLEDGE T M

2 Political Landscape Obama faced the highest unemployment rate of any re-elected elected president in 76 years Our competitive advantages: President Obama s record and policies Our opponent s record and policies Field organization and base of support Digital strategy Data, analytics and technology 2

3 Analytics and Data Strategy What is our goal? To re-elect elect President Obama How do we achieve that goal? Win 270 electoral votes What can we affect to achieve that goal? Voter registration, persuasion, turnout and voter protection How can we use analytics and data to achieve our goal? OFA Analytics and Data Strategy 3

4 A Brief History of Political Data and Analytics NYT Jan 12, 1968 Data sources and tools Individual-level level voter files Precinct-level election results Demographics Polling and ad testing What changed? Data Computing power Modeling science Research, testing and experiments Inside the Secret World of the Data Crunchers Who Helped Obama Win

5 Recent History of Political Data and Analytics Number of OFA/DNC Analytics Staff

6 Election Results Gore: 48.4% 266 EVs 51.0M votes Bush: 47.9% 271 EVs 50.5M votes Obama: 52.9% 365 EVs 69.5M votes McCain: 45.7% 173 EVs 60.0M votes Kerry: 48.3% 251 EVs 59.0M votes Bush: 50.7% 286 EVs 62.0M votes Obama: 51.1% 332 EVs 65.9M votes Romney: 47.2% 206 EVs 60.9M votes 6

7 The Team Who we hired Buy-in process How we organized Cave ethos and rules 7

8 The Data Public Voter files Election results Census Public polls Internal Field Online (BarackObama.com, subscribers, online advertising, social media) Internal polling Commercial Consumer data 8

9 Obama Analytics: What We Did What We Did Purpose Polling Conducted 30k interviews per week Update individual-level predictive models State of the race modeling Social media Ad testing and modeling Sentiment analysis Fundraising Volunteer recruitment Monte Carlo simulations Facebook targeted sharing TV, online, direct mail, telephone modeling Analyzed online sentiment towards candidates, surrogates and events Built individual-level models for direct mail, telemarketing, events and online fundraising Constructed individual-level models Identify changes in state of the race at the state level for the purpose of redirecting resources among states Leverage our Facebook network to target the most effective ads to the right people through their friends Identify at the individual level who to target with each ad in each medium Determine which candidates and campaign surrogates were most effective with each audience Identify the individuals who can be targeted most profitably in each medium to maximize overall fundraising Optimize volunteer recruitment and efficient use of volunteers 9

10 Goal: Increase Support Among Likely Voters and Turnout Among Likely Supporters Turnout Persuasion GOTV Among those who are likely to vote, we want to increase support Among those who support our candidate, we want to increase turnout Support 10

11 Predicting the Results Source Obama Romney Margin Michael Barone Romney +92 George Will Romney +104 Karl Rove Romney +32 Dick Morris Romney +112 OFA Analytics Obama +126 FiveThirtyEight Obama +126 Intrade Obama +126 Actual result Obama

12 Election Results: 2008 and 2012 Largest number of votes in a US election o 69.5M (Obama 2008) o 65.9M (Obama 2012) o 62.0M (Bush 2004) Largest vote percentage for a Democratic presidential candidate since 1964 o 52.9% (Obama 2008) o 51.1% (Obama 2012) o 50.1% (Carter 1976) New records set in 2012 o2m volunteers made 146 million volunteer door knocks and calls o4m donors gave $1B+ o36.9m Facebook likes, 37.5M Twitter followers omost tweeted photo ever (800k re- tweets) Largest vote percentage for any presidential candidate since 1988 o 52.9% (Obama 2008) o 51.1% (Obama 2012) o 50.7% (Bush 2004) 12

13 Key Campaign Lessons Use actual outcomes Build individual-level level predictive models Target at the individual level Conduct randomized controlled experiments Validation process 13

14 Challenges and Solutions: Lessons for the Corporate World Getting buy-in Demonstrate how data and analytics supports the mission Show value Data resides in silos Start with data that is readily accessible Match across databases to add value over time Lack of familiarity with language and tools Translate into language your audience understands focus on outcomes Training and education Resources and time Develop quick, inexpensive tests Begin implementation early 14

15 The Typical Buy-In Process What do key decision-makers care about? What do they need to make decisions? Identify promising datasets Get to know the data Recruit allies and champions Design userfriendly tools Show results quickly Focus on goals, deliverable, outcomes Share credit 15

16 Analytics and Big Data Strategy for Corporations What are we trying to do? What are our goals? What can we affect to reach those goals? How can analytics and data help us do this? Role of Data Mining 16

17 Big Data, Survey Data and Predictive Modeling in a Corporate Environment Big Data Sales, marketing, advertising data CRM data Online data ( , online ads, Website) Social media data Consumer data (Acxiom, Experian) Small Data Small Data Sampling Predictive + + Modeling = Surveys Field experiments A/B testing Data and models can be updated continuously Individual-Level Targeting Sales, Revenue, Profit Corporate Reputation 17

18 Summary of Analytics and Data Tools 1) Analytics Strategy Development: What are our goals? What can we affect to reach those goals? How can we use analytics and data to do this? 2) Internal Data Audit: What data do you have? Where does it live? What are you missing? What data is most valuable? What data hygiene and bias problems do you face? 3) External Data Testing: What s out there? What is it worth? (quantify return on investment) 4) Data Integration: Combining data across platforms online, offline, sales, marketing, advertising, CRM, etc. 5) Technology Tools: Facilitating, automating processes and reducing errors for better analysis and decision-making. 18

19 Summary of Analytics and Data Tools Continued 6) Survey Research and Message Testing: What messages are most effective and should be tested in creative executions? What data can we collect on corporate reputation and other intangibles? 7) Ad Testing: In any format (online ads, social media, SMS, , direct mail, TV, telemarketing) what is our return on investment? How many dollars in revenue do we gain for each dollar of ad spending? 8) Experiments: What return are we getting on our current investments? How can we improve our messages, offers, creative execution or targeting? 9) Individual-Level Predictive Modeling: For each individual, what communication (sales, marketing, advertising, etc.) is most effective what message/offer/product do we deliver, who do we deliver it to and how do we deliver it? (creative execution and medium) 19

20 Summary of Analytics and Data Tools Continued 10)Online/Offline Integration: How do we use online data to drive offline activity and viceversa? 11)Social Media and Other Online Data: What can we learn by mining Twitter? What can we learn about people who follow us, friend us, like us, link to us? 12)Simulators: What tools can we provide to decision-makers to help them identify the optimal price, offer, message and measure the effect on unit sales, revenue and profit? 13)Reporting, Visualization and Mapping: Do decision-makers have insight into what is going on? Do they know what products, offers or teams are over/under-performing? What can we show them with quarterly, monthly, weekly or daily reports to help them make better decisions? 20

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