Customer Clustering Based on Customer Purchasing Sequence Data

Abstract

Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.