應用模糊關聯規則建立連鎖咖啡店之顧客價值模型

APPLYING FUZZY ASSOCIATION RULES TO ESTABLISH CUSTOMER VALUES MODEL FOR COFFEE-SHOPS

10.6338/JDA.202312_18(3).0003

蔣文育(Chiang, Wen-Yu)

顧客價值 ； RFM Model ； Fuzzy K-Means ； RFMDT模型 ； Apriori演算法 ； Customer Values ； RFM model ； Fuzzy K-Means ； RFMDT model ； Apriori algorithm

Journal of Data Analysis

18卷3期（2023 / 12 / 01）

59 - 78

繁體中文;英文

本研究目的在於建構與改良顧客關係管理中的顧客價值模型（Recency, Frequency and Monetary, RFM model），經由模型的建立可精確的掌握連鎖咖啡店產業市場之顧客價值，進而提升咖啡產業之顧客價值。本文以台北市之連鎖咖啡店消費顧客為研究對象，研究方法採用模糊聚類分析法（Fuzzy K-Means, FKM）對消費顧客進行市場區隔分析，分群結果發現連鎖咖啡店消費之三個硬性市場與其模糊市場：成本考量、方便考量、與專業考量。並以改良式之Recency, Frequency, Monetary, Discount, and Turnover（RFMDT）模型導向監督式Apriori演算法進行分析，建構4項連鎖咖啡店顧客價值關聯規則、與2項模糊關聯規則，此模型建議可套入顧客關係管理軟體之學習功能中，達到對顧客價值之認知品質提升，並可作為連鎖咖啡產業行銷決策之方案基礎。

The objective of this research is to improve and establish customer value (Recency, Frequency, and Monetary, RFM model) of Customer Relationship Management (CRM). Customer value can be handled and promoted precisely by the established model. The research objects are experienced shoppers of chain store within six months in Taipei city. The researcher uses Fuzzy K-Means (FKM) method to process market segmentation to find four clusters. Their features are: convenience consideration, café quality, and cost consideration. The Recency, Frequency, Monetary, Discount, and Turnover (RFMDT) model (revised based on RFM) is applied on the Apriori algorithm to establish four association rules and two fuzzy association rules. This model can be employed in the learning software of CRM system in order to identify and enhance customer value of the café chain store industry.

1. 林陽助,林秀貞,李宜致(2007)。體驗行銷、顧客滿意度與顧客中程度關係之研究—以大台北地區連鎖咖啡店為例。顧客滿意學刊，3(2)，57-94。
   連結：
2. 羅巧芳,吳信宏,張啟恩,鄭易英(2008)。應用資料探勘於戶外活動用品專賣店之顧客忠誠及價值分析。品質學報，15(4)，293-303。
   連結：
3. 經濟部，https://www.moea.gov.tw，2022/03
4. USDA (2022), U.S. Department of Agriculture, https://www.usda.gov 2022/12
5. Bezdek, J. C.(1981).Pattern Recognition with Fuzzy Objective Function Algorithms.New York:Plenum Press.
6. Breiman, L.,Friedman, J.H.,Olshen, R.A.,Stone, C.J.(1984).Classification and Regression Trees.New York:Chapman and Hall.
7. Brown, M.,Pope, N.,Voges, K.(2003).Buying or Browsing? An Exploration of Shopping Orientations and Online Purchase Intention.European Journal of Marketing,37(11),1666-1684.
8. Calnatone, R. J.,Sawyer, A. G.(1978).The Stability of Benefit Segments.Journal of Marketing Research,15,395-404.
9. Chiang, W. Y.(2021).Using a data-driven marketing strategy on customer relationship management: an empirical case of urban coffee shops in Taiwan.British Food Journal,123(4),1610-1625.
10. Churchill, G. A., Jr.(1979).A Paradigm for Developing Better Measures of Marketing Constructs.Journal of Marketing Research,16(1),64-73.
11. Churchill, G. A., Jr.(1995).Marketing Research: Methodological Foundations.New York:Dryden Press.
12. Cohen, J.,Cohen, P.(1975).Applied Multiple Regression/Correlation Analysis for the Behavior Sciences.New York:Lawrence Erlbaum.
13. Dillon, T. W.,Reif, H. L.(2004).Factors Influencing Consumers’ E--Commerce Commodity Purchases.Information Technology, Learning, and Performance Journal,22(2),1-12.
14. Dunn, J. C.(1946),“Indices of Partition Fuzzy and Detection of Clusters in Large Data Sets”, Elsevier, New York.
15. Dunn, J. C.(1974).A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters.Journal of Cybernetics,3,32-57.
16. Eklund, T.,Back, B.,Vanharanta, H.,Visa, A.(2003).Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking.Information Visualization,2,171-181.
17. Goodman, J.(1992).Leveraging the Customer Database to your Competitive Advantage.Direct Marketing,55(8),26-27.
18. Gruijter, J. J.,McBratney, A. B.(1988).A Modified Fuzzy K Means for Predictive Classification.Classification and Related Methods of Data Analysis,Amsterdam:
19. Huang, C. C.,Nuangjamnong, C.(2022).Analyzing Factors Influencing Customer Satisfaction towards Customer Purchase Intention in Louisa Coffee, Taiwan.Global Scientific and Academic Research Journal of Multidisciplinary Studies,1(9),19-33.
20. Hughes, A.(2005).Strategic Database Marketing.New York:McGraw-Hill.
21. Kelly, K.(2006).,New York:Jupiter Research.
22. Kohonen, T.(1990).The Self-organizing Map.Proceedings of the IEEE,78(9),1464-1480.
23. Krishnapuram, R.,Kill, J. M.(1993).A Possibilistic Approach to Clustering.IEEE Fuzzy System,1(2),98-110.
24. Lee, G. G.,Lin, H. F.(2006).Customer perceptions of e-service quality in online shopping.International Journal of Retail and Distribution Management,33(2),161-176.
25. Likert, R. (1932), “A Technique for the Measurement of Attitudes”, Archives of Psychology, 140, 1-55.
26. Linoff, G. S.,Berry, M. J. A.(2002).Mining the Web: Transforming Customer Data into Customer Value.N.J.:John Wiley and Sons.
27. Linoff, G. S.,Berry, M.l J. A.(2004).Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management.N.J.:John Wiley and Sons.
28. MacQueen, J. B.(1967).Some Methods for Classification and Analysis of Multivariate Observations.Proceeding of 5th Symposium Mathematical Statistics and Probability
29. Mazanec, J. A.(1995).Positioning Analysis with Self-Organizing Maps.Cornell Hotel and Restaurant Administration Quarterly,36(6),80-95.
30. Miglautsch, J.(2002).Application of RFM Principles: What to do with 1-1-1 Customers?.Journal of Database Marketing,9(4),319-324.
31. Nunnally, J. C.(1978).Psychometric Theory.New York:McGraw-Hill.
32. Roiger, R. J.,Geatz, M. W.(2003).Data Mining: A Tutorial-Based Primer.New York:Pearson Education Inc..
33. Roubens, M(1982).Fuzzy clustering algorithms and their cluster validity.European Journal of Operational Research,10,294-301.
34. Ruspini, E.H.(1969).A New Approach to Clustering.Information Control,15,22-32.
35. Russell, S.,Lodwick, W.(1999).Fuzzy Clustering in Data Mining for Telco Database Marketing Campaigns.North American Fuzzy Information Processing Systems
36. Selim, S. Z.,Ismail, M. A.(1984).Soft Clustering of Multidimensional Data: a Semifuzzy Approach.Pattern Recognition,175,559-568.
37. Shih, Y. Y.,Liu, C. Y.(2003).A Method for Customer Lifetime Value Ranking-Combining the Analytic Hierarchy Process and Clustering Analysis.Journal of Database Marketing and Customer Strategy Management,11(2),159-172.
38. Trauwaert, E.(1988).On the Meaning of Dunn’s Partition Coefficient for Fuzzy Clusters.Fuzzy Sets and Systems,25,217-242.
39. Wei, W.,Mendel, J. M.(1994).Optimality Test for Fuzzy c-means Algorithm.Pattern Recognition,27(11),1567-1573.
40. Xie, X. L.,Beni, G.(1991).A Validity Measure for Fuzzy Clustering.IEEE Transactions of Pattern Analysis and Machine Intelligence,13,841-847.
41. Xu, Y.,Paulins, V. A.(2005).College Students’ Attitudes Toward Shopping Online for Apparel Products: Exploring a Rural Versus Urban Campus.Journal of Fashion Marketing and Management,9(4),420-433.
42. Zadeh, L. A.(1965).Fuzzy Sets.Information and Control,8,338-353.
43. 王秀螢(2000)。國立東華大學企業管理研究所。
44. 王宗琳(2003)。國立中山大學傳播管理研究所。
45. 王素梅(2008)。食品工業發展研究所調查報告食品工業發展研究所調查報告,未出版
46. 吳萬益(2005).企業研究方法.台北:華泰書局.
47. 林朝興,唐瑩荃(2006)。以顧客價值分析與權重建盡探勘來進行協力式音樂推薦。資訊、科技與社會，6(1)，1-26。
48. 郭若宣(2020)。銘傳大學應用統計與資料科學學系碩士班。
49. 陳彤生(2002)。運用改良 RFM 提升行銷效益的實證研究。第七屆人工智慧與應用研討會論文集
50. 陳虹君,張舜德(2003)。銷售資料挖掘與顧客關係管理整合之研究─以製藥業為例。2003 電子商務與數位生活研討會論文集
51. 黃俊英,林震岩(1997).SAS 精析與實例.台北:華泰書局.
52. 黃靜芳(2005)。國立東華大學企業管理研究所。
53. 詹雅婷(2004)。大葉大學休閒事業管理學系。
54. 蔣文育(2009)。網路購物產業電子化行銷模式之建構─以服飾與化妝品類產品為例。中小企業發展季刊，11，1-26。
55. 蔣文育(2012)。應用顧客價值模型與資料探勘技術於資料庫行銷規劃：以咖啡連鎖店為例。觀光旅遊研究學刊，7(2)，61-71。