NDLI: A Knowledge Discovery Based Customization Services Model and Empirical Analysis

Content Provider	IEEE Xplore Digital Library
Author	Guoling Lao Zhaohui Zhang
Copyright Year	2007
Description	Author affiliation: Shanghai Univ. of Finance & Econ., Shanghai (Guoling Lao)
Abstract	It is not feasible and practical to offer "one to one" customer service to every customer. Customer segmentation is needed urgently before drawing customization service strategy. Most stockjobbers in China classify their customers into three main groups: big customer, secondary customer, ordinary customer, according to the customers' possessions they declared. But this classification approach is not reasonable and effective enough, as not all wealthy customers bring high contribution value to the stockjobber while some customers who do not have a high level of investment capital account for large amounts of stockjobber's profits. This article shed lights on the common characteristics of the high-valued customer. After analyzing the classical CLV model, we find that customer potential contribution value to the stockjobbers largely depends on some key factors. At the basis of this, whether a new customer has high contribution value to the stockjobber in the future can be accurately predicted in their entering phase. The result of the empirical study validated this and it also reveals that it's possible and reasonable to differentiate high CPV customer and low CPV ones through quantitative analysis.
Starting Page	1
Ending Page	6
File Size	2239675
Page Count	6
File Format	PDF
ISBN	1424408849
DOI	10.1109/ICSSSM.2007.4280305
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Engineering drawings Finance Predictive models customer potential value knowledge discovery Information management Customer service Data mining Information analysis customer segmentation Databases securities industry Economic forecasting
Content Type	Text
Resource Type	Article

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