Design of an intelligent customer identification model in e-Commerce logistics industry

Abstract

The emergence of e-commerce in recent years has lead to revolutionary changes in the logistics industry, as e-commerce relies heavily on efficient logistics to deliver the online goods to customers in a short period of time. Compared with traditional logistics, e-commerce orders, with a high variety of goods but small in quantity, are generally received from large number of customers worldwide. With a huge customer base, it is challenging for logistics service providers (LSPs) to provide satisfactory time-critical logistics services to meet the diversified customer requirements. In order to differentiate its services from others e-commerce LSPs, it is important to identify potential target groups of customers, and their behaviour so as to attract their attention. In this paper, an intelligent customer identification model (ICIM) is designed to support data analysis for managing customer relationships in a systematic way. The ICIM integrates the k-means clustering algorithm and the C4.5 classification algorithm in order to be able to deal with both continuous and discrete attributes for extracting valuable hidden knowledge. This effectively supports the identification of actual customer needs, and the classification of new customers in the future with minimum time for developing customer relationship management (CRM) recommendations to customers, thus improving business performance. Through a pilot study in a freight forwarding company in Hong Kong, it provides a real world demonstration and validation of data mining for CRM in the emerging e-commerce logistics industry.