Using CRM Data for Customer Loyalty Prediction – A Case Study in Electrical &; Electronic Industry

碩士 === 國立中山大學 === 資訊管理學系研究所 === 102 === Due to the advances in information and internet technology, customers nowadays can easily acquire information about prices and specifications of desired products. Thus, how to differentiate from other similar products in the market or even customized products...

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Bibliographic Details
Main Authors: Feng-Cheng Shieh, 謝豐成
Other Authors: S.Y. Hwang
Format: Others
Language:zh-TW
Published: 2014
Online Access:http://ndltd.ncl.edu.tw/handle/rk5kd7
Description
Summary:碩士 === 國立中山大學 === 資訊管理學系研究所 === 102 === Due to the advances in information and internet technology, customers nowadays can easily acquire information about prices and specifications of desired products. Thus, how to differentiate from other similar products in the market or even customized products has become a key factor for companies that try to thrive in today’s competitive market. To do so, companies need to understand their customers, and many of them have employ CRM related systems to collect data about their customers and subsequently perform analysis on the data. The intention is to retain the customers by establishing customer loyalty, which will in turn reduce the cost of maintaining customers and marketing. In this research, we focus on the electric and electronic (EE) industry and analyze the CRM database from the Taiwan branch of an international EE firm To allow for more comprehensive analysis, we further include inquiry and marketing information to predict customer’s loyalty. Our research findings as listed as follows: 1. Customers in machine building etc. industry and service times are positively related to loyalty because more service times allow the company to interact more with the customer and subsequently build trust. 2. The transaction waiting time is reversely related to loyalty. Note that in EE industry, product prices are usually high and their specifications are complicated. Loyal customers rely more on the company’s professional judgment and thus spend less time on making their own judgment.