Academic journal article Journal of Digital Information Management

A New Customer Classification Algorithm for Electronic Commerce Enterprises

Academic journal article Journal of Digital Information Management

A New Customer Classification Algorithm for Electronic Commerce Enterprises

Article excerpt

1. Introduction

Customer relations management is one of the core problems of modern enterprises, whose customer oriented thought requires CRM system to be able to effectively obtain various kinds of information of customers, identify all the relations between the customers and enterprises and understand the transaction relation between customers and enterprises; meanwhile, deeply analyze customers' consuming behavior, find customers' consumption characteristics, providing personalized service for customers, supporting the decisions of enterprises. The three basic problems CRM needs to solve are how to get customers, how to keep customers and how to maximize customer value, among which maximizing customer value is the ultimate purpose, getting customers and keeping customers are both the means for realizing the purpose. The core of analyzing the three problems CRM needs to solve is to classify customers. "Getting Customers" and "Retaining Customers" need to ascertain which customers are attainable, which customers need to be kept, which customers are kept for a long term and which customers are kept for a short term, therefore, customer classification is needed. It is the same case with "Maximizing Customer Value". Due to different values of different customers, "Maximum Customer Value" of different customers should be distinguished. Thus, the core problem of enterprises to correctly implement CRM is to adopt effective method to reasonably classify customers, find customer value, focus on high-value customers with enterprises' limited resources, provide better service for them, keep "High-value"" customers for loss prevention; also, establish the customer service system through classification, carry out differential customer service management. Hence, customer classification is becoming a more and more popular research hotspot, also a research difficulty, becoming one of the urgent problems of CRM [1].

2. Summarization of Customer Classification Methods

The widely-used methods of enterprises for customer classification at present are mainly qualitative method and quantitative method [2-9]. As the qualitative method for customer classification is just to classify all the target customers of enterprises in the macroscopic level, customer classification is carried out according to different value emphasis of different customers. The formation of customer value is simply expressed as: Value = Benefit--Cost. Qualitative classification method classifies customers in a simple way, only offering guidance for customer classification of enterprise in the macroscopic level, unable to provide specific and credible basis for enterprise decisions; furthermore, as there is no strict process of argumentation, the method depends on decider's subjective inference, there may be certain deviations in the analysis process, easily resulting in faulty decisions. For this reason, to truly provide customer classification information beneficial to enterprises should depend on quantitative technology for customer classification [3, 4, 5].

Quantitative classification method is to apply quantitative analysis technology to conduct customer classification on the basis of some specific customer variables (credit level of customers, purchasing power of customers, characteristics of demand of customers, etc.). Currently, there are mainly two categories of data mining for quantitative customer classification research, which are traditional statistical method and non-statistical method. The former mainly includes cluster analysis, Bayesian Classification, factor analysis method, etc.; this statistics-based method is unable to process a great deal of sophisticated customer data, and there are some problems on the accuracy of customer classification results, so to fundamentally solve the problem of customer classification needs to rely on non-statistical customer classification method, which mainly includes neural network, fuzzy set method, association rules, genetic algorithm, etc. …

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