Proposal of customer segmentation method based on the impact of each feature on outcome variable

Abstract

Customer segmentation is important for implementing appropriate marketing strategies to meet the different needs of each customer group. The purpose of customer segmentation is to improve the effectiveness of marketing strategies by implementing appropriate measures for each segment, and the formation of similar segments is required to determine the factors that determine the effectiveness of the measures. However, conventional methods do not fully consider this. Therefore, in this study, we propose a method of clustering similar customers based on the impact of feature variables on the effectiveness of measures by using SHAP value vectors, which are known as interpretation methods for machine learning models. This allows us to consider the similarity of the factors that determine the effectiveness of measures, making it possible to implement the most effective measures for each customer segment. We conducted experiments using artificial and actual data to demonstrate the effectiveness of the proposed method.