Mastering Marketing Measurement: Incrementality, Attribution, and Media Mix Modeling

Chief Advertiser

Michael Kaminsky is the Co-founder and Co-CEO of Recast, a marketing measurement platform that helps brands optimize their ad spend. As a trained econometrician, he led the marketing science team at Harry’s, where he developed data-driven strategies to enhance customer acquisition and retention. Michael also held analytics positions at Case Commons and Analysis Group.

In this episode…

Marketers often struggle to measure tangible results from their advertising strategies. As brands scale and expand across multiple channels, traditional attribution models like last-click fall short, leading to inaccurate ROI estimates and poor budget decisions. How can marketing leaders measure what works to make informed, data-driven decisions at scale?

According to marketing measurement expert Michael Kaminsky, brands can measure marketing performance by adopting a causal, experiment-backed approach to media mix modeling (MMM). Each MMM should uncover incrementality, the true causal impact of media spend. Michael recommends starting with simple triangulation using first-touch, last-touch, and post-checkout surveys, then layering in lift tests to validate assumptions. These assumptions should be validated consistently using experimentation to align MMMs with real-world results.

In today’s episode of Chief Advertiser, Samir Balwani hosts Michael Kaminsky, Co-founder and Co-CEO of Recast, to discuss employing media mix modeling to measure marketing performance. Michael talks about performing incrementality testing, utilizing first-touch, last-touch, and post-checkout survey data for attribution, and the difference between causal and inferred MMM.