The Promise and Peril of Big Data
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The Obama 2012 campaign brought the benefits of big data to the forefront of political campaigns and many other organizations in D.C. Hear how Enroll America has incorporated these models into their ...

The Obama 2012 campaign brought the benefits of big data to the forefront of political campaigns and many other organizations in D.C. Hear how Enroll America has incorporated these models into their digital media efforts that drive insurance exchange enrollments and what they’ve learned about its limitations. Adam will offer his advice for your organizations as you face the big data questions.

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The Promise and Peril of Big Data Presentation Transcript

  • 1. The Promise and Peril of Big Data Or Trust but Verify Mid-Atlantic Marketing Summit April 24, 2014
  • 2. Who Is Enroll America? • Landmark Patient Protection and Affordable Care Act passed into law in 2010. • In 2011 Enroll America was founded to ensure that all Americans enroll in, and retain, health coverage. • To achieve their mission Enroll first had to find the uninsured. • Enter big data…
  • 3. Building a model Started by thinking about the individual: • Used public and commercial data • Geography State >> City >> ZIP >> Household • Demographics Race, Age, Marital status Purchasing habits, Medical history Surveyed a sample and built the model: • Surveyed 10,000+ people • Asked insurance status • Individual likelihood of uninsured score • Strong predictors Age, gender, race, income, voting history
  • 4. 30 44 52 59 37 32 35 47 41 44 Micro-targeting down to the household possible
  • 5. How big data informed decisions What we learned: • Two-thirds of the uninsured live in 12 states • Half live in just 114 counties (<4% of all counties) • A lot of education needed • The more informed, the more likely to sign up • Required a longer, multi- touch approach What we did: • 10 target states, incl ZIPs • Field teams in each state • Started knocking on doors of likely uninsured • The model held up early on • Where else can we apply the model?
  • 6. Uninsured microtargeting media strategy • $5M campaign with a mix of display, mobile, search, social • Test multiple tactics with a heavy emphasis on BIG DATA • Targeted select ZIPs in 10 target states, same as Field • Maximize the number of email addresses acquired at lowest possible CPA • Demo-geo-targeting and re-targeting/behavioral attribute targeting (i.e. uninsured, intent to enroll)
  • 7. AUGUST SEPTEMBER OCTOBER 1 Research revealed core motivations: • Personal stories • Financial security • Just the facts • Affordability • Cuz mom says so Responsive website and digital ad campaign Countdown to October 1
  • 8. Research-driven online experience The site: • Wordpress platform • Responsive design • Custom subsidy calculator How we built it: • Agile style approach to design and development • Functional prototypes instead of static page comps • Iterative reviews • Designers and developers often collaborating side by side
  • 9. Research-driven creative
  • 10. Last minute rush to produce TV • Last week of September • Seven interviews • Two TV spots highlighting the personal impact of affordable health insurance • The spots launched in target markets in time for the beginning of open enrollment
  • 11. Not the results we expected • Personal stories didn’t resonate the way we thought they would. People cared more about how insurance would impact them personally as well as its affordability • After a few weeks, we realized our budget was horribly misaligned— forgot to prioritize basic Direct Response tactics • Let the promise of Big Data lead our strategy instead of informing it • Confused voter and consumer: ROI justifies the political campaign investment but that’s rarely true in consumer marketing
  • 12. But why didn’t it work? Big Data is expensive and (for us) too targeted • We had a relatively small media budget given the size of the opportunity • Big Data media had to work a lot harder relative to lower cost alternatives • Didn’t reach influencers
  • 13. Recalibration and the results • Higher volume at lower costs • Find the low hanging fruit • Display & Lead Gen: Simple geo (ZIP) and demo (age, gender, income) targeting • Paid search: ZIP targeting • Paid social: Targeting lookalike audiences • Retargeting of site visitors • 2 million unique site visitors • 1 million consumer email addresses in 10 states • Connected 380,000 people to their marketplace or application assistance via digital only
  • 14. Optimization worked $- $20 $40 $60 $80 $100 $120 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 1-Oct 8-Oct 15-… 22-… 29-… 5-Nov 12-… 19-… 26-… 3-Dec 10-… 17-… 24-… 31-… 7-Jan 14-… 21-… 28-… 4-Feb 11-… 18-… 25-… 4-Mar 11-… 18-… 25-… EffectiveCPA ConsumerEmailsAcquired Campaign CPA Consumers Emails
  • 15. Recalibrated the website too From stories to information and assistance
  • 16. Takeaways Trust but verify Don’t let strategy be dictated by the newest, flashiest thing Is a Big Data driven media strategy right for you? • Goals & Budget • Accessibility of your target audience • Does the marginal return justify the cost
  • 17. Adam Stalker National Digital Director | Enroll America Email: astalker@enrollamerica.org Twitter: @adamtstalker LinkedIn: linkedin.com/in/adamstalker Leigh George, PhD Vice President | Social@Ogilvy Email: leigh.george@ogilvy.com Twitter: @leighgeorge LinkedIn: linkedin.com/in/leighgeorge Connect with us