Influencer Targeting In Less Than 2 Minutes

Over the last few weeks we have tackled one of the biggest recent developments in public affairs advocacy—how AI has upended expensive, black box forms of influencer targeting. This week, we pull it all together in a less than 2 minute summary.

Influencer Targeting Defined

Influencer targeting is a high-impact tactic to reach the influencers of policymakers. The goal is to reach not just the decision-maker, but the people the decision-maker listens to. 

Most Influencer Targeting Is A Black Box

Several companies sell products that they market as data sets they have compiled of various elected officials' influencers. There are many catches, but the two most important are:

• The data sets are EXTREMELY expensive.
• The vendors WON'T TELL YOU WHO IS IN THEM.

Influencer Targeting: Step 1

The first step in successful influencer targeting is to geofence the decision-maker's neighbors and colleagues. We do this by drawing precision geofences around the decision-maker's personal neighborhood and offices. Like this:

 . . . and this . . .

Influencer Targeting: Step 2

The next step in successful influencer targeting is to find the unknown influencers of the decision-maker, using the latest in AI. Natural language processing (NLP) is a subfield of AI that focuses on enabling machines to understand human language. It's what underlies ChatGPT, Siri, Alexa and other AI tools you're familiar with. 

NLP transforms influencer targeting because it allows us to find and reach people who consume content about particular elected officials, issues, bills, or other topics.

So imagine you're trying to reach Congressman Brett Guthrie's influencers. Now imagine an important influencer to Congressman Guthrie who never consumes content about him. It's impossible to do. So NLP creates a digital breadcrumb trail of his influencers.

That's successful influencer targeting in less than 2 minutes—faster, less expensive, and more precise than ever.

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