改良RFM模式預測零售客戶價值

A Modified RFM Approach for Predicting Retail Customer Value

林榮禾(Rong-Ho Lin)；莊淳凌(Chun-Ling Chuang)；莊薇薇(Wei-Wei Chuang)

海量資料 ； RFM模式 ； 顧客價值 ； K-means分群法 ； Big Data ； RFM Model ； Customer Value ； K-means Cluster Analysis

顧客滿意學刊

12卷2期（2016 / 09 / 01）

235 - 249

繁體中文

企業的管理，需要分層負責，並設定各部門管理指標，分別進行考核，並於每月/季/年，依照執行績效，逐一檢討，並區分個別職責，確認工作績效；特別是零售業，動輒幾數百萬的會員，加上百萬筆的交易資料，如何去分別管理，分層負責，一直以來都是這行業的管理者一個很大的挑戰，在眾多的客戶關係管理(CRM)的分析模式中，RFM模型是被廣泛提到的，這個模型是衡量客戶價值和客戶創利能力的重要工具和手段。該模型透過一個客戶的近期購買行為、購買的總體頻率以及花了多少錢三項指標來描述該客戶的價值狀況。本研究主要的目的在於應用相關理論來提出零售業專用RAA模式的分析法，並使用臺灣超市會員海量資料庫的資料來進行分析，不採用舊式抽樣方式，藉此可以更精準的瞄準客戶，進行客戶服務，作為企業績效考核標準。

Each company's management needs decentralized and sets each department management metrics assessment every month / quarter / year according to executive performance and distinguishes between individual responsibilities to confirm work performance of various departments. How to separately manage and decentralize several hundred millions of members and one million transaction information has been a great challenge for retail industry managers. This study explores how to find customer value and professional division of labor to assist enterprises improving customer service and performance review. This study uses Taiwan supermarket membership data repository for analysis.

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