Customer loyalty, return and churn prediction through machine learning methods : for a Swedish fashion and e-commerce company

Abstract: The analysis of gaining, retaining and maintaining customer trust is a highly topical issue in the e-commerce industry to mitigate the challenges of increased competition and volatile customer relationships as an effect of the increasing use of the internet to purchase goods. This study is conducted at the Swedish online fashion retailer NA-KD with the aim of gaining better insight into customer behavior that determines purchases, returns and churn. Therefore, the objectives for this study are to identify the group of loyal customers as well as construct models to predict customer loyalty, frequent returns and customer churn. Two separate approaches are used for solving the problem where a clustering model is constructed to divide the data into different customer segments that can explain customer behaviour. Then a classification model is constructed to classify the customers into the classes of churners, returners and loyal customers based on the exploratory data analysis and previous insights and knowledge from the company. By using the unsupervised machine learning method K-prototypes clustering for mixed data, six clusters are identified and defined as churned, potential, loyal customers and Brand Champions, indecisive shoppers, and high-risky churners. The supervised classification method of bias reduced binary Logistic Regression is used to classify customers into the classes of loyal customers, customers of frequent returns and churners. The final models had an accuracy of 0.68, 0.75 and 0.98 for the three separate binary classification models classifying Churners, Returners and Loyalists respectively.