Marketing Segmentation Through Machine Learning Models

Abstract

Customer relationship management (CRM) aims to build relations with the most profitable clients by performing customer segmentation and designing appropriate marketing tools. In addition, customer profitability accounting (CPA) recommends evaluating the CRM program through the combination of partial measures in a global cost—benefit function. Several statistical techniques have been applied for market segmentations although the existence of large data sets reduces their effectiveness. As an alternative, decision trees are machine learning models that do not consider a priori hypotheses, achieve a high performance, and generate logical rules clearly understood by managers. In this article, a three-stage methodology is proposed that combines marketing feature selection, customer segmentation through univariate and oblique decision trees, and a new CPA function based on marketing, data warehousing, and opportunity costs linked to the analysis of different scenarios. This proposal is applied to a large insurance marketing data set for alternative cost and price conditions showing the superiority of univariate decision trees over statistical techniques.