Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy

TL;DR

This paper introduces an iterative causal segmentation algorithm designed to improve marketing strategies by effectively managing tightly coupled causal variables and confounders, bridging the gap between market segmentation and marketing strategy.

Contribution

The paper presents a formally proven iterative causal segmentation algorithm that addresses challenges in causal ML for marketing involving coupled treatment variables and confounders.

Findings

Algorithm effectively manages tightly coupled causal variables.

Bridges gap between market segmentation and marketing strategy.

Addresses challenges in causal ML for marketing applications.

Abstract

The field of causal Machine Learning (ML) has made significant strides in recent years. Notable breakthroughs include methods such as meta learners (arXiv:1706.03461v6) and heterogeneous doubly robust estimators (arXiv:2004.14497) introduced in the last five years. Despite these advancements, the field still faces challenges, particularly in managing tightly coupled systems where both the causal treatment variable and a confounding covariate must serve as key decision-making indicators. This scenario is common in applications of causal ML for marketing, such as marketing segmentation and incremental marketing uplift. In this work, we present our formally proven algorithm, iterative causal segmentation, to address this issue.