整合分類與分群技術於物流業高價值顧客流失預測模型之研究

Integrating Classification and Clustering Techniques for Customer Churn Prediction in Logistics Industry

謝易澄(Yi-Cheng Hsieh)

指導教授 ： 胡雅涵

國立中正大學/管理學院/資訊管理學系/碩士(2013年)

摘要

顧客價值與顧客流失一直以來都是企業最關注的重要議題，在物流業界也是如此。近年來隨著網際網路的興起、消費者意識的抬頭，再加上同業在市場上的競爭，使得顧客的生命週期變得比過去更加短暫。如何與有價值的顧客建立長期的合作關係，則是在現今競爭的市場當中求穩定的關鍵。對於企業而言，與顧客建立穩固的合作關係是重要的，但是成本卻是昂貴的。當企業的資源有限，就必須針對高價值顧客進行顧客保留，才會獲得更高的效益。
本研究的第一部分是執行資料萃取，以擴充的顧客價值分析模型、物流的顧客滿意度指標等規則與定義，建構出新的且適合用來評估顧客價值與顧客流失的研究變項。第二部分則是從顧客價值的研究變項當中，透過資料探勘的分群技術，區隔出高、低價值顧客群。第三部分再從高價值的顧客群，透過資料探勘的分類技術，進行顧客流失預測的分析。最後，藉此發掘出影響顧客流失的最重要關鍵因素，以提供企業決策者制定對應的行銷策略。

關鍵字

資料探勘 ； 流失分析 ； 物流顧客服務 ； RFM分析

並列摘要

Customer value and churn has always been the most important issue of concern to enterprise, in the logistics industry as well. In recent years, with the rise of the Internet, the rise of consumer awareness, coupled with horizontal competition in the market, making the life cycle of the customer becomes more short-term than in the past. How to establish long-term cooperative relationship with valuable customers, is the key to stability in today's competitive market. For businesses, it is important to establish a stable partnership with customers, but the cost is expensive. When the corporate resources are limited, high-value customer retention will get higher benefits.
In the first part of this study, we perform data extraction to historical transaction data. By extended customer value analysis model and customer satisfaction index for logistics, construct a new and appropriate research variables to assess the customer value and churn. In the second part, using data mining clustering techniques to separate the high and low value customers by the customer value research variables. In the third part, using data mining classification techniques to performed churn prediction analysis for high-value customers. Finally, discover the key factors of customer churn to provide business decision-makers to develop a marketing strategy.

並列關鍵字

Churn analysis ； RFM analysis ； Logistics customer service ； Data Mining

參考文獻

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