Abstract
Applying big data technology, this study presents a customer segmentation method of Electronic Toll Collection (ETC) based on vehicle behavioral characteristics. A segmentation index system of ETC customers comprising Recency, Frequency, and Monetary is extracted and constructed using ETC data. The whole-sample clustering analysis of ETC customers is accomplished with the Clustering LARge Applications (CLARA) algorithm while overcoming the invalidation problem of big data clustering. A decision tree on ETC customer segmentation is constructed and transformed into a set of segmentation rules. Empirical results indicate that the proposed method is better able to analyze travel characteristics and to present values and appreciation potentials for ETC customer classification. This method provides an innovative idea for implementing precision marketing and establishing hierarchical discount rates for ETC customers. Furthermore, it provides theoretical support to increase the ETC customer scale and payment ratio, thus improving the decision-making level in expressway operation and management.
Highlights
Electronic Toll Collection (ETC) is an essential part of the Intelligent Transportation System (ITS)
The results indicated that the limited capacity of manual toll gates could lead to queues spill back, interfering and reducing ETC gates capacities [22]
The 2014 annual ETC data, over 31 million, of passenger vehicles with seven seats or fewer in Shaanxi province was chosen as basic data
Summary
Electronic Toll Collection (ETC) is an essential part of the Intelligent Transportation System (ITS). By the end of February 2017, 29 of 31 provinces in mainland China (except Tibet and Hainan) had realized networking of expressway ETC and cumulatively built 14,285 ETC lanes, 1,115 selfsupporting service centres, and 37,502 cooperative agency centres. Since the 1990s, along with remarkable development in customer-oriented management, Customer Relationship Management (CRM) proposed by the Gartner Group Consulting Company has attracted extensive attention worldwide [2, 3]. CRM provides reliable, comprehensive, and complete understanding for enterprises through the application of emerging technologies to integrate customer data efficiently, helpfully maintaining and expanding a mutually beneficial relationship between customers and enterprises. Aiming to allocate service resources rationally and implement customer strategies accurately, customer segmentation classifies and evaluates types of customers, providing theoretical and methodological guidance for enterprises’ gain of higher commercial value for customers
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