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Big data panel slides

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Big data panel slides

  1. 1. 2
  2. 2. MediaButton
  3. 3. Splash page experimentVariations: Button: Media: • Sign Up 1. Get Involved Image • Learn More 2. Family Image • Join Us Now 3. Change Image • Sign Up Now 4. Barack’s Video 5. Springfield Video 6. Sam’s Video
  4. 4. Button: “Sign Up”
  5. 5. Button: “Learn More”
  6. 6. Button: “Join Us Now”
  7. 7. Button: “Sign Up Now”
  8. 8. Splash page experimentVariations: Button: Media: • Sign Up 1. Get Involved Image • Learn More 2. Family Image • Join Us Now 3. Change Image • Sign Up Now 4. Barack’s Video 5. Springfield Video 6. Sam’s Video
  9. 9. Media: “Get Involved”
  10. 10. Media: “Family”
  11. 11. Media: “Change”
  12. 12. Media: “Barack’s Video”
  13. 13. Media: “Springfield Video”
  14. 14. Media: “Sam’s Video”
  15. 15. Splash page experimentVariations: Button: Media: 1. Sign Up 1. Get Involved Image 2. Learn More 2. Family Image 3. Join Us Now 3. Change Image 4. Sign Up Now 4. Barack’s Video 5. Springfield Video 6. Sam’s Video
  16. 16. Splash page experiment results
  17. 17. Splash page experiment results
  18. 18. Splash page experiment results
  19. 19. Splash page experiment results Email Volunteers Amount Raised SubscriptionsOriginal: 7,120,000 712,000 $143,000,000+40.6% +2,880,000 +288,000 +$57,000,000 New: 10,000,000 1,000,000 $200,000,000
  20. 20. Splash page experiment results Email Volunteers Amount Raised SubscriptionsOriginal: 7,120,000 712,000 $143,000,000+40.6% +2,880,000 +288,000 +$57,000,000 New: 10,000,000 1,000,000 $200,000,000
  21. 21. Splash page experiment results Email Volunteers Amount Raised SubscriptionsOriginal: 7,120,000 712,000 $143,000,000+40.6% +2,880,000 +288,000 +$57,000,000 New: 10,000,000 1,000,000 $200,000,000
  22. 22. Email me: Follow me:dan@optimizely.com @dsiroker
  23. 23. 26
  24. 24. Modeling is using the data you have about avoter to make an informed judgment about: - whether they will vote - who they will vote for - what issues affect their vote - any other question you think has predictable (reproducible) behaviors
  25. 25. humans make models all the time, as wecollect data & make informed judgments: 80% ? likelihood of voting for Obama
  26. 26. SURVEY GOP=94% (94/100)STATEWIDE GOP~94% (94,000/100,000)50 years old white56% 59% 63% 68% 85% 89% 94% % GOP
  27. 27. - knowledge management- master data management- data harmonization- voter relationship managementOBAMA CAMPAIGN’S PROJECT NARWHAL
  28. 28. Slowly lowering the wall between online & offline data 1. Data providersWilliam Alexander Lundry, 2. Targeted Display AdsRegistered Republican 3. Facebook Apps 4. Volunteered Association
  29. 29. The Conservative Data Ecosystem RNC Data Trust Themis United In Purpose
  30. 30. 33
  31. 31. Big Political Data for the masses
  32. 32. Periodic to real time
  33. 33. Responds to DM Likelihood to vote Partisan Affiliation Top Issue Likelihood to unsubscribeLikelihood to volunteer Best Channel for GivingReceptiveness to treatment
  34. 34. 39
  35. 35. The world of surveyresearch is changing rapidly.
  36. 36. The average phone surveyresponse rate is around 20% and declining.
  37. 37. 27% of UShouseholds arecell only. (CDC)
  38. 38. 46% of American adults own a smartphone.
  39. 39. Telephone ConsumerProtection Act of 1991
  40. 40. Online research tools are emerging.
  41. 41. Whether online works as a tooldepends on what we want to measure.
  42. 42. As it gets harder to ask... What if we get better about listening?
  43. 43. Sentiment? I can go totwittersentiment.appspot.com and get an analysis.
  44. 44. Does “Who said it - Newt or Buzz Lightyear?” reallycount as a positive tweet?
  45. 45. “Gingrich had 6 percent more activity than the other candidates and the positive sentiment on him related to Super Tuesday is at 84 percent. Sentiment in general online conversation about him is only at 45 percent. So it seems his folks arePhoto: Marc Grob for Time working the online world hard.” -From POLITICO “Playbook,” Super Tuesday (March 6 2012), quoting an email from a Washington-based public affairs consultant
  46. 46. Survey Sentiment Analysis• Landline bias • Online/activist bias• Contained universe • Variable universe• Concrete results • Subject to interpretation• “Snapshot” in time • Real-time, evolving• Message testing • Identify new trends

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