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All Hail Donald Trump & His Facebook Marketing - Trusted Conf

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By utilising advanced segmentation, data-driven content, machine learning and bid optimisation, Donald Trump’s Facebook campaign was simply incredible. As marketers, there are several key takeaways, indicating the future (and power) of paid digital advertising.

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All Hail Donald Trump & His Facebook Marketing - Trusted Conf

  1. 1. 1 All Hail Donald Trump
  2. 2. 2 [ And The Facebook Campaign That Helped To Get Him Elected ]
  3. 3. 3 “The key difference is that the Trump campaign experimented with ads on Facebook in a way no campaign had ever done before” Wired.com
  4. 4. 4 Meet Brad Parscale The “Secret Weapon” ● Primarily building websites for 20 years ● Built websites for various Trump organisations since 2010 ● 2016 invited to build a website for Trump’s Presidential Campaign ● Ends up runnings his digital marketing campaign for the election ● Worked as a one-man-show, from home, at the start of the campaign
  5. 5. 5 “The art behind the trump digital campaign was translating data to content” Brad Parscale
  6. 6. 6 The 3 Pillars 1. Data 2. Brief Creatives 3. Testing
  7. 7. 7 1.Data ● Started with RNC Database of 200,000,000 ● How people will vote, and what issues matter most to them ● Understand audience ● Create and segment audiences “Data is the arrow pointing you in which direction to go” Brad Parscale
  8. 8. 8 2.Brief Creatives ● Audiences and segments go to content writers and linguists ● Develop content that aligns to audience ● Animations, video, text, colours, fonts, etc. “Make audience understand they needed change” Brad Parscale
  9. 9. 9 3.Testing ● You’re always learning ● Figure out what people consume ● Gather learnings, produce even better content ● Done hourly at times “Data drove content production” Brad Parscale
  10. 10. 10 3.Testing
  11. 11. 11 3.Testing
  12. 12. 12
  13. 13. 13
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  15. 15. 15 3.Testing ● The Trump campaign constantly tested minute variations in the design, color, background and phrasing of Facebook ads, in order to maximize their impact. ● Typically 50,000 to 60,000 variations were tested each day ● Maximum variations in one day hit 150,000
  16. 16. 16 Data Brief Creatives Testing RNC Database ● Learnings ● Audiences ● Segmentation ● Issues ● Understand Audiences & Segments ● Develop content geared towards them ● Consistent testing ● Gather learnings Store ● Store learnings to database ● Machine learning
  17. 17. 17 Data Brief Creatives Testing RNC Database ● Learnings ● Audiences ● Segmentation ● Issues ● Understand Audiences & Segments ● Develop content geared towards them ● Consistent testing ● Gather learnings Store ● Store learnings to database ● Machine learning Facebook Lookalike Audiences
  18. 18. 18 www.inmarketingwetrust.com.au Facebook Lookalike Audiences ▹ Starts from a “Custom Audience” ▸ Customer File - Email, Phone Numbers, ZIP/Postal Code, Name, User ID, etc. ▸ Website Traffic ▸ App Activity ▸ Offline Activity ▸ Engagement - Facebook video, Facebook page, Instagram profile, Event ▹ Build Lookalike Audience off of Custom Audience ▸ Tell Facebook how ‘precise’ the lookalike audience should be ▸ The more ‘precise’ the smaller the size. The less ‘precise’, the larger the audience ▸ Utilises Facebook’s machine learning to find individuals who “look like” your custom audience ▸ The larger your custom audience, the more precise the data can get
  19. 19. 19 www.inmarketingwetrust.com.au Facebook Lookalike Audiences Example: RNCDB-INFRA-TEXAS-1 RNCDB-INFRA-TEXAS-2 RNCDB-INFRA-TEXAS-3 RNCDB-INFRA-TEXAS-4 RNCDB-INFRA-TEXAS-5 RNCDB-INFRA-OHIO-1 RNCDB-INFRA-OHIO-2 RNCDB-INFRA-OHIO-3 RNCDB-INFRA-OHIO-4 RNCDB-INFRA-OHIO-5 FIND SIMILAR USERS FIND SIMILAR USERS RNCDB-INFRA-TEXAS-LOOKALIKE RNCDB-INFRA-OHIO-LOOKALIKE
  20. 20. 20 www.inmarketingwetrust.com.au Facebook Lookalike Audiences Example: RNCDB-INFRA-TEXAS-LOOKALIKE RNCDB-INFRA-OHIO-LOOKALIKE Clicked Ad Featuring Hillary Clicked Ad Featuring Trump Clicked Ad Featuring Hillary Clicked Ad Featuring Trump FIND SIMILAR USERS FIND SIMILAR USERS RNCDB-INFRA-TEXAS-LOOKALIKE HILLARY-CTA-LOOKALIKE RNCDB-INFRA-TEXAS-LOOKALIKE TRUMP-CTA-LOOKALIKE RNCDB-INFRA-OHIO-LOOKALIKE HILLARY-CTA-LOOKALIKE RNCDB-INFRA-OHIO-LOOKALIKE TRUMP-CTA-LOOKALIKE
  21. 21. 21 www.inmarketingwetrust.com.au Google Similar Audiences ▹ Google offers the same thing as Facebook’s Lookalike Audiences ▹ Called “Similar Audiences” ▹ Example: ▸ Create a remarketing list for users visiting pages about ‘Tours in Europe’ ▸ Once the list has, ideally, more than 500 users ▸ Google will automatically create a list ‘Similar to Visited Europe Tours Page’ ▸ The more data you have, the more you allow Google’s machine learning to find similar users
  22. 22. 22 Learnings from data then translated to “on the ground” efforts
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  24. 24. 24 ISSUE #1 ISSUE #2 ISSUE #3 ISSUE #4 ISSUE #5 FACEBOOK USERS IN TEXAS DATA [ENGAGEMENT, SHARES, CLICKS, ETC.] DRIVE OFFLINE ACTIVITY Data Drives Offline Activity
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  26. 26. 26 How would they get CPMs so low?
  27. 27. 27 Facebook’s algorithms prioritise more engaging content
  28. 28. 28 If Facebook’s model thinks your ad is 10 times more likely to engage a user than another company’s ad, then your effective bid at auction is considered 10 times higher than a company willing to pay the same dollar amount.
  29. 29. 29 www.inmarketingwetrust.com.au Facebook Relevance Score ▹ Determined after 500 impressions ▹ Can fluctuate daily ▹ Ranges from 1 (bad) to 10 (great) ▹ Based on engagement ▹ A relevancy score increase ▸ Likes, clicks, shares, comments, app installs, video views ▹ A relevancy score decrease ▸ Someone clicks “I don’t want to see this ad” or doesn’t click on the ad ▹ Indicates how relevant the ad is to your audience ▹ The higher the relevance score, the better ‘discount’ you receive at auction
  30. 30. 30 www.inmarketingwetrust.com.au Google Quality Score ▹ Ranges from 1 (bad) to 10 (great) ▹ Based on 3 factors: ▸ Expected Clickthrough Rate (Most important) ▸ Ad Relevance (How relevant is the ad to the search query) ▸ Landing Page Experience (Is the overall experience good? Does the page line up with the search query and ad?) ▹ The higher the quality score, the better ‘discount’ you receive at auction
  31. 31. 31 www.inmarketingwetrust.com.au Google Quality Score
  32. 32. 32 www.inmarketingwetrust.com.au Summary ▹ Data drove content production ▹ Content was designed to help audience understand that they want change ▹ Multivariate testing based on ad engagement ▹ Drive down CPMs ▹ Data was not siloed; instead, learnings went to “on the ground efforts” ▹ Parscale actually led: ▸ Digital ▸ TV ad buys ▸ Phone campaigns ▸ Door knocking ▸ Social media ▸ Fundraising ▹ All based from data learnings from Facebook

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