Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach

计算机科学 潜在Dirichlet分配 多项式logistic回归 营销 人工智能 机器学习 主题模型 业务
作者
Kamil Matuszelański,Katarzyna Kopczewska
出处
期刊:Journal of Theoretical and Applied Electronic Commerce Research [Multidisciplinary Digital Publishing Institute]
卷期号:17 (1): 165-198 被引量:64
标识
DOI:10.3390/jtaer17010009
摘要

This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers located with zip codes. At the modelling stage, we apply machine learning extreme gradient boosting and logistic regression. The quality of models is verified with area-under-curve and lift metrics. Explainable artificial intelligence represented with a permutation-based variable importance and a partial dependence profile help to discover the determinants of churn. We show that customers’ propensity to churn depends on: (i) payment value for the first order, number of items bought and shipping cost; (ii) categories of the products bought; (iii) demographic environment of the customer; and (iv) customer location. At the same time, customers’ propensity to churn is not influenced by: (i) population density in the customer’s area and division into rural and urban areas; (ii) quantitative review of the first purchase; and (iii) qualitative review summarised as a topic.
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