The document discusses how Brad Parscale helped run Donald Trump's digital marketing campaign in 2016. It describes how the campaign used data from the RNC database to create targeted audiences on Facebook. It then tested thousands of ad variations daily to maximize engagement. This extensive testing and use of data allowed the campaign to lower costs per thousand impressions. The lessons learned from digital ads were then applied to offline campaign activities as well.

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All Hail Donald Trump

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[ And The Facebook Campaign That Helped To Get Him Elected ]

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“The key difference is that the Trump campaign
experimented with ads on Facebook in a way no
campaign had ever done before”
Wired.com

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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

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“The art behind the trump digital campaign was
translating data to content”
Brad Parscale

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The 3 Pillars
1. Data 2. Brief Creatives 3. Testing

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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

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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

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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

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3.Testing

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3.Testing

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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

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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

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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

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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

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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

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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

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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

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Learnings from data then translated to
“on the ground” efforts

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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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How would they get CPMs so low?

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Facebook’s algorithms prioritise
more engaging content

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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.

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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

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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

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Google Quality Score

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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