A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study

TL;DR

This study combines statistical and machine learning methods to create an interpretable model analyzing online customer behavior, providing insights for targeted marketing and logistics improvements at a major Chinese retailer.

Contribution

It introduces a novel hybrid approach that enhances interpretability of machine learning models in customer behavior analysis, with practical recommendations for marketing and logistics strategies.

Findings

Product choice is insensitive to delivery time but affects order quantity.

Discount effectiveness varies by product class and discount size.

Price and demographics are key drivers of customer behavior.

Abstract

We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output. This analysis involves identifying what features and characteristics have the most significant impact on customers' purchase behavior, thereby enabling us to predict future sales with a high level of accuracy, and identify the most impactful variables. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We also show that the effectiveness of various discounting methods depends on the specific product and the discount size. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools. Customers' choice behavior across different product classes is mostly driven by price, and to a lesser extent, by customer demographics. The former finding asks for exercising care in deciding when and how much discount should be offered, whereas the latter identifies opportunities for personalized ads and targeted marketing. Further, to curb customers' batch ordering behavior and avoid the undesirable Bullwhip effect, JD should improve its logistics to ensure faster delivery of orders.