  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.
