An algorithmic marketing approach to analyzing consumer well-being: Incorporating psychological factors in customer loyalty

Abstract

In recent years, there has been growing interest in consumer well-being in marketing research. This study examines psychological loyalty, which connects corporate profits with consumer well-being, and proposes an algorithmic marketing approach to analyze survey data from the Matsuya Ginza Department Store to identify specific variables that impact consumer well-being. To clarify the structure between each variable and consumer well-being, we considered various gradient boosting machine learning models, which emphasize classification accuracy for qualitative data, and constructed an ensemble learning model. We also conducted clustering on Matsuya Ginza customers, analyzed the variables that significantly contribute to consumer well-being in different clusters, and developed specific measures to improve products and services. Furthermore, using SHAP (Shapley Additive Explanations) and ICE (Individual Conditional Expectation), we conducted instance-level analysis to show to what extent consumer well-being tends to increase or decrease in relation to important variables for each instance.