Why Attribution Models Miss the Value of Influencer Content

Episode notes

In this episode of Marketing Analytics with Fexingo, Lucas and Luna explore why traditional attribution models undervalue influencer content. Using the example of a micro-influencer campaign for a DTC skincare brand, they break down the problem: influencer posts often drive delayed, cross-device conversions that last-touch models misattribute to search or direct traffic. They walk through a realistic case where a beauty brand saw a 37 percent increase in branded search volume after influencer posts, yet the model credited almost none of that lift to the influencers. The hosts discuss practical fixes, including view-through attribution windows, incrementality testing with geo holdouts, and building custom models that account for halo effects. If you're a marketer trying to measure influencer ROI beyond likes and comments, this episode offers a clear framework for capturing the true value of creator partnerships.

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