資料採礦於顧客關係管理之應用 -以建材零售業為例

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Title:	資料採礦於顧客關係管理之應用 -以建材零售業為例
Other Titles:	Applying Data Mining to Customer Relationship Management -A study on Construction Materials Retail Business
Authors:	施旻慧 Shih,Min-Hui
Contributors:	洪堯勳;胡坤德 Hon,Jau-Shin;Hu,Kun-Te 工業工程與經營資訊學系
Keywords:	顧客關係管理;關聯法則;二階層集群分析;資料採礦;建材零售業 Customer Relationship Management;Association Rule;Two-Step Cluster;Data Mining;Construction Materials Retail Business
Date:	2013
Issue Date:	2014-02-19T07:01:12Z (UTC)
Abstract:	由於零售業者面臨動態的競爭環境，再加上消費者對產品需求多樣化的消費模式，因此顧客關係管理逐漸受到企業重視。過去的傳統資料處理方式已跟不上現在的速度，利用資料採礦的方式來做資料的處理及分析，可以有效的發掘出資料隱藏著看不見的重要訊息，將資訊做更大價值的發揮。本研究以建材零售業為研究對象，針對購買建材的廠商與需要做裝潢的顧客，企圖了解是否能透過結合80/20法則與關聯法則提升忠誠顧客比例，以及透過關聯法則分析顧客基本資料與歷史交易資料，了解顧客的購買行為進而為顧客量身訂作客製化產品及服務，進而與顧客建立良好的互動關係。本研究首先將資料採礦與顧客關係管理之概念結合，以建立出研究之架構。其次，將初始的資料透過資料轉換與定義，轉換為使用者需要的結構化的資料，使分析者與系統皆能判讀。利用二階層群集分析來對於顧客做分群，分群出80/20兩群顧客以及流失與未流失兩群顧客。最後透過資料採礦的關聯法則，採用Apriori演算法萃取出對於企業有意義的法則，同時根據法則的判讀做出解釋並提供建議及因應的方式，可作為經營決策者制定行銷策略的參考。 As retailers face dynamic comptetion and continuous consumer’s demands for products consumption modes of diversification. Therefore, Customer Relationship Management (CRM) is gaining more and more attention in the enterprise. The traditional ways of data processing is out of date; and have been replaced with Data Mining which is used to. not only extrapolate data but also reveals the meaning behind the raw data collected in such a manner the information can be interpreted and utilized.This research focuses on the study of building materials retail business, from the perspective of the manufacters who purchase of the construction materials and the customers who need to do the interior decorating. Attempt to understand the Pareto Principle whether through a combination of the 80/20 rule and association rules to enhance the proportion of loyal customers. As well as through the association rule to analysis the basic information of customer and the transaction data to understand the customer’s buying behavior and provide customers tailor-made products and services. And establish a good interaction relationship with customers.First, we will be combining the concept of data mining and customer relationship management in building the main structure of this study. Transforming the raw data obtained through data switching and data identification into materials which are acceptable by both analysts and the system. Using the Two-Steps Cluster to categorize the customer and then grouping of 80/20 into two groups and the customer churn. Finally, through the Association Rule in data mining, we will calculate the most significant rule for this company by Apriori algorithm. Meanwhile we also provide the explanation of the rule, suggestions and solutions as well as derive an optimal marketing strategy.
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