1. Lessons from Inside the Obama Analytics Cave: Targeted Marketing, Ad Testing and Digital Strategies Andrew Claster Former Deputy Chief Analytics Officer Obama for America

2. Agenda 2 1.Inside the Obama Analytics Cave 2.Targeted Marketing, Ad Testing and Digital Strategies

3. Analytics and Data Strategy 3 1. What is our goal? 2. How do we achieve that goal? 3. What can we affect to achieve that goal? 4. How can we use analytics and data to achieve our goal? 1. Re-elect President Obama 2. Win 270 electoral votes 3. Voter registration, persuasion, turnout and voter protection 4. OFA Analytics and Data Strategy

4. The Data 4 Voter files Election results Census Public polls Public Field Online (BarackObama.com, email subscribers, online advertising, social media) Internal polling Internal Consumer data Commercial

5. What We Did 5 Purpose SurveysState of the race, message testing, building individual-level models Ad testing – online, TV, direct mail Identify most effective messages, creative executions and platforms Ad buy optimizationOptimize TV ad purchasing Individual-level predictive modeling and targeting Identify most responsive individuals for each message, creative execution and mode of contact and deliver targeted communications at the individual or household level ExperimentsCalculate ROI for each mode of contact Sentiment analysisAnalyze importance and sentiment surrounding events and issues ReportingProvide visibility to decision-makers regarding performance Social media targeted sharing Leverage social networks of supporters to increase reach and effectiveness

6. TV Advertising 6 April to November Ad Buys – Broadcast and National Cable Only Source: Washington Post, Kantar Media/CMAG Democrats spent 11% less on TV ads But Democratic ads aired 12% more

7. Agenda 7 1.Inside the Obama Analytics Cave 2.Targeted Marketing, Ad Testing and Digital Strategies

8. Analytics & Big Data Strategy 8 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

9. The Data – Healthcare Public Relations and Marketing 9 Census Public health data Public Customer and provider data – services, payments Online and offline Historic sales, marketing and advertising data Internal Consumer data Commercial

10. Predictive Modeling Big Data, Survey Data and Predictive Modeling 10 Big Data Small Data ++= Individual-Level Targeting Sales, Revenue, Profit Corporate Reputation Data and models can be updated continuously Sales, marketing, advertising data CRM data Online data (email, online ads, Website) Social media data Consumer data (Acxiom, Experian) Sales, marketing, advertising data CRM data Online data (email, online ads, Website) Social media data Consumer data (Acxiom, Experian) Big Data Small Data Sampling Surveys Field experiments A/B testing

11. How Can Data and Analytics Help With Targeted Marketing, Ad Testing and Digital Strategy? 11 Who should we be targeting? Renewal, retention, lapsed customers, prospects, upselling What products, offers and messages should we be using?To whom should we target these products, offers and messages? How do we develop, test and deliver effective creative, messages, offers, products? In what media should we deliver this creative, messaging, offer, product? Email, online ads, direct mail, telemarketing, Website How do we measure return on investment? Goal: Deliver the right creative with the right products, offers and messages to the right targets in the right media and accurately forecast and measure results

12. Key Elements 12 Conduct randomized controlled experimentsUse actual market outcomes (e.g. profit, revenue, unit sales)Build individual-level predictive modelsTarget at the individual levelValidation process

13. Successful Analytics and Data Strategies 13 Leadership and buy-inWhat does leadership care about most? What don’t they know?Look for quick/easy win opportunities aligned to goalsRecruit allies and championsIdentify promising datasetsGet to know the dataMaintain focus on goals, results and deliverables

14. 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. 14

15. Analytics and Data Tools 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, email, 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) 15

16. Analytics and Data Tools 10. Online/Offline Integration: How do we use online data to drive offline activity and vice-versa? 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? 16