Summary of Causalmmm: Learning Causal Structure For Marketing Mix Modeling, by Chang Gong et al.

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

by Chang Gong, Di Yao, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su, Jingping Bi

First submitted to arxiv on: 24 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper introduces Causal Marketing Mix Modeling (CausalMMM), a novel approach to predict Gross Merchandise Volume (GMV) in online advertising. Traditional methods, such as regression techniques, struggle to handle marketing complexity. To overcome this limitation, the authors propose CausalMMM, which automatically discovers interpretable causal structures from data and yields better GMV predictions. The approach addresses two key challenges: causal heterogeneity among different shops and marketing response patterns, including carryover and shape effects. By integrating Granger causality in a variational inference framework, CausalMMM measures causal relationships between channels and predicts GMV with regularization of temporal and saturation marketing response patterns. Experimental results show significant improvements in synthetic datasets (5.7-7.1%) and enhanced GMV prediction on an e-commerce platform.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper helps online stores make better decisions about advertising by predicting how much money they’ll make based on their ads. Right now, computers use simple methods to do this, but these methods don’t work well when there are many different factors at play. The authors propose a new way to solve this problem, called Causal Marketing Mix Modeling (CausalMMM). This approach finds the underlying patterns in data and uses them to make more accurate predictions. The method addresses two key issues: that different shops have unique patterns, and that ads can have lasting effects on customers. By using Granger causality and a special framework, CausalMMM measures how different ads affect each other and makes predictions about how much money stores will make. The results show that this approach is more accurate than current methods.