AI-Driven Marketing Mix Modeling

Name: AI-Driven Marketing Mix Modeling
Uploaded: 2022-03-18
Duration: 60 min
Description: Allergan/AbbVie's marketing sciences team on moving marketing mix modeling from linear regression to gradient boosting with Shapley value attribution.

Predictive AI
H2O Driverless AI

Overview

In this webinar, Akhil Sud and Vijay Raghavan from Allergan/AbbVie's marketing sciences team join H2O.ai's Dr. Michael Proctor to explain why they moved their marketing mix modeling from traditional linear regression to a gradient boosting model with Shapley value attribution. They cover the core limitations of regression-based attribution, including its assumptions about linear response curves, ad-stock decay, and channel interactions, and share validation tests and client results showing where the GBM-plus-Shapley approach outperforms. The discussion also covers practical caveats: no model fully accounts for unexpected market events, and results still need to hold up against business judgment.

Key takeaways

Why traditional linear regression for marketing mix modeling requires assuming a functional form (linear response, geometric ad-stock decay) and manually testing interaction terms one at a time, which breaks down once dozens of digital channels are added.
How the team used an H2O AutoML-based GBM approach with Shapley value attribution, rooted in Lloyd Shapley's game theory work, to model non-linear response curves and channel interactions without pre-specifying them.
How controlled tests with known "correct" answers showed the GBM-plus-Shapley method matched linear regression on purely linear relationships but was substantially more accurate on non-linear and interaction-driven scenarios, for example correctly attributing 540 out of 540 coins of impact versus regression's 503.
That building a linear marketing mix model took the team two to three weeks per model, versus under a third of that time with the GBM approach.

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