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    政大典藏 > College of Commerce > MBA Program > Theses >  Item 140.119/112827


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/112827


    Title: 運用RFM模型結合資料採礦預測潛在顧客提升行銷效益-以Y藥局為例
    Using RFM Model and Data Mining to Predict Potential Consumers and Improve Marketing Efficiency－A Case Study of Y Pharmacy
    Authors: 吳岱芸
    Wu, Dai Yun
    Contributors: 鄭宇庭
    吳岱芸
    Wu, Dai Yun
    Keywords: 資料採礦
    顧客終生價值
    RFM模型
    Data mining
    Customer lifetime value
    RFM model
    Date: 2017
    Issue Date: 2017-09-13 16:00:53 (UTC+8)
    Abstract: 本研究根據過去藥局經營文獻中，所提出之藥局經營關鍵因素進行數據實證，以了解在過去訪談或其他質化研究中所整理之藥局經營關鍵因素是否能實際影響藥局的經營狀況。
    　　由研究結果，利用Y藥局之POS資料實證：具有不同特性的消費族群對Y藥局而言，具有不同的顧客價值以及消費行為，實證過去文獻所提出之結論。
    　　而針對不同客群的分群方式，本研究利用購買品類的頻率及數量，將顧客做集群分析，共分出五種類型：家庭育兒族、新婚未孕族、高齡保健族、新生兒養育族及愛美小資族。整體而言愛美小資族因為購買產品之特性，有較高的顧客終生價值，且在購買行為上，以購買多樣性的表現顯著高於其他族群，但在購買頻率及近期性表現則較差，顯示出該族群有較多的可能至其他通路購買。而連鎖藥局的主要顧客，家庭育兒族，則有最低的顧客價值，在購買行為上品類較為單一，數量零碎雖購買行為頻繁，卻未能實際創造價值。
    　　另外針對藥局經營關鍵因素也發現，藥局在活動促銷上，主要能促進消費者的購買頻率及縮短消費者購買的近期性，整體而言是能提升短期的消費次數，但在金額上卻未有較顯著的相關性，也顯示了金額的促銷並未能持續將顧客價值提升，僅能刺激短暫的消費行為。
    　　針對未來相關研究之建議也認為，透過資料採礦，能有效將實際銷售資料轉化為消費行為的刑為變數，為未來藥局經營做更多實際數據的驗證，改善過去多使用店長經驗及質性訪談方式衡量經營成效之狀況　。
    As the concept of the Customer Relationship Management (CRM) is getting more and more popular. The analysis way of data mining used not only extrapolating data but revealing the meaning of what the customer will think and what kind of customer will be the most valuable.
    This research focuses on the study of utilizing the model of RFM which meaning regency, frequency and monetary. The research not only using three measures but also adding the profit of each customer to find who the most valuable one is. And establish a better way to cluster the customer then finding the best marketing strategy for them. According to understand the pareto principle of the 80/20 rule, the research combine RFM model and cluster to measure the customer value. As well as analyzing the basic information of customer and the transaction data to understand the customer’s buying behavior and provide customer suitable products and services.
    The result showed that, the different types of pharmacy customers have different customer value which confirming the past researches. According to the research purposes we also clustering the pharmacy customer in five types which are family, newly-married, petty budget, elder and pregnancy. And the result showed that the petty budget is the most valuable cluster in all of the pharmacy customers.
    Reference: 一、 中文文獻
    1. Carman, J. M. & K. P. Uhl, (1973)著，林富松譯，(1979)。行銷學：原理與方法，台北市：徐氏基金會。
    2. 行政院主計處，2016，中華民國行業標準分類
    3. 郭佩雯，2004，連鎖藥局關鍵成功因素的探討，國立台灣大學公共衛生學院醫療機構管理研究所碩士論文。
    4. 陳秀津，2001，台灣地區連鎖業追求卓越與其績效關聯性之研究，國立彰化師範大學商業教育學系在職進修專班學位論文。
    5. 經濟部統計處，2010，批發、零售及餐飲業經營實況調查報告。
    6. 經濟部統計處，2011，批發、零售及餐飲業經營實況調查報告。
    7. 經濟部統計處，2012，批發、零售及餐飲業經營實況調查報告。
    8. 經濟部統計處，2013，商業經營實況調查報告。
    9. 經濟部統計處，2015，批發、零售及餐飲業經營實況調查報告。
    10. 經濟部商業司，1996，連鎖店經營管理實務
    11. 謝邦昌，2002，聚焦 Data Mining： Data Mining 觀念, 方法及技術, 應用實例 (上篇)，中國統計，(5)，51-52。
    12. 謝邦昌、鄭宇庭，2015，資料採礦之技術及應用—Excel實例演練，新陸書局股份有限公司。
    13. 謝邦昌、鄭宇庭、蘇志雄，2009，Data Mining概述—以Clementine 12.0為例，中華資料採礦協會。
    14. 謝邦昌，2001，資料採礦入門及應用─從統計技術看資料採礦，資商訊息顧問有限公司。
    15. 黃惠如，1993。藥局經營連鎖大逆轉。突破，101，pp.112-4
    16. 臺灣連鎖暨加盟協會，1996，1995連鎖店年鑑，台北市：連鎖暨加盟協會。
    17. 臺灣連鎖暨加盟協會，2003，2002連鎖店年鑑，台北市：連鎖暨加盟協會。
    二、 英文文獻
    1. Boote, A. S. (1981), Marketing Segmentation by Personal Value and Salient Product Attributes. Journal of Advertising Research, New York, Feb 1981; Vol. 21, Iss. 1; pp. 29, 7 pgs.
    2. Chiu, T., D. Fang, J. Chen, Y. Wang & C. Jeris, (2001), A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database Environment. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp263-268.
    3. Fayyad, M. U. (1996), Data mining and knowledge discovery: Making sense out of data. IEEE Expert, 11(10), pp.20-25.
    4. Fayyed, U., G. Piatetsky-Shapiro & P. Smyth, (1996), From Data Mining to Knowledge Discovery in Databases. AI Magazine Volume 17 Number 3, pp.40-42.
    5. Fraley, C. & A. E. Raftery, (1998), How many Clusters? Which Clustering Method? Answers via Model-based Cluster Analysis. Computer Journal, 4, pp.578-588.
    6. Greenfeld, N. (1996), Data mining. UNIX Review, 14(5), pp.9-14.
    7. Krzystof, C., P. Witold & S. Roman, (1998), Data Mining: Methods for Knowledge Discovery. Kluwer Academic Publishers, Boston.
    8. McCarthy, J. E. (1981), Basic Marketing:A Managerial Approach. Homewood, IL: Richard D. Irwin Inc., 7th ed.
    9. Schiffman, L. G. & L. L. Kanuk, (1994), Consumer Behavior, 5ed, Prentice Hall International, Inc.
    10. Wendell, R. S. (1956), Product Differentiation and Market Segmentation as Alternative Marketing Strategies. Journal of Marketing, Vol.21.
    11. Wind. Y., (1978), Issue and Advances in Segmentation Research. Journal of Marketing Research, vol.15, pp.317-337.
    12. Yankelouich, D. (1964), New Criteria for Market Segmentation. Harvard Business Review, 42(2), pp.14-21.
    Description: 碩士
    國立政治大學
    企業管理研究所(MBA學位學程)
    104363112
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104363112
    Data Type: thesis
    Appears in Collections:[MBA Program] Theses

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