Research Area:  Machine Learning

Abstract:

Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.

Keywords:  

Author(s) Name:  Maciej Pondel, Maciej Wuczy?ski, Wieslawa Gryncewicz, ?ukasz ?ysik, Marcin Hernes, Artur Rot, Agata Kozina

Publisher name:  ResearchGate

DOI:  10.52825/bis.v1i.42

Volume Information:  Volume: 4, (2025)

Paper Link:   https://www.researchgate.net/publication/352946623_Deep_Learning_for_Customer_Churn_Prediction_in_E-Commerce_Decision_Support