電腦運算功能日益增強，企業過去至今也累積龐大之數據資料，資料探勘技術隨之蓬勃發展。企業逐漸意識到透過資料探勘方式對於決策的價值，本研究取用某線上食品零售業者交易數據進行資料探勘，以提出顧客關係管理方案。
    本研究根據電商4P之概念建構顧客關係管理模式，以RFM指標對線上食品零售公司的顧客進行兩階段K-means分群，形成4種具有顯著差異的顧客群體，再以決策樹CART以及Apriori法對顧客群進行資料探勘。
    根據研究結果，集群分析將顧客區分為「鮮肉型顧客」、「沉睡巨人型顧客」、「忠誠型顧客」、「流失型顧客」四群，進一步透過決策樹CART與Apriori法掌握各群顧客特徵與產品購買關聯性，以期作為日後企業對顧客群廣告投放、行銷預測及服務策略擬定之參考依據。

Computer computing functions are increasing day by day. The companies has also accumulated huge amounts of data in the past. This has led to the popularization of data mining technology. Many companies are gradually realizing the value of data mining for decisions. This research used the transaction data of the online food retailer for data mining to propose a customer relationship management plan.
    This research is based on concept of e-commerce 4P to construct a customer relationship management model. Using RFM indicators for two-stage K-means clustering on customers of online food retail company. There were 4 types of customer groups with significant differences formed. Then use the decision tree CART and the Apriori method to mine the data of the customer groups.
According to the research results, cluster analysis divides customers into 4 groups: fresh meat customers, sleeping giant customers, loyalty customers and churning customers. Further, through the decision tree CART and the Apriori method, grasp customers characteristics and product relevance. It’s expected to be used as a reference for advertising, marketing prediction and service strategy in the future.

目錄
目錄 I
圖目錄 II
表目錄 III
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的	3
第三節 研究流程	3
第二章 文獻探討	5
第一節 電子商務	5
第二節 顧客關係管理與RFM模型 9
第三節 目標客群與集群分析 12
第四節 決策樹分析與Apriori法 14
第三章 研究方法 17
第一節 分析流程 17
第二節 資料來源與變數提取 18
第三節 分析方法	21
第四節 使用軟體介紹 27
第四章 實證分析 29
第一節 資料整理	29
第二節 顧客分群	34
第三節 預測模型	44
第五章 結論與建議 51
第一節 研究發現	51
第二節 研究結論	52
第三節 研究限制與建議 54
參考文獻 55

圖目錄
圖1-1研究流程圖 4
圖3-1分析流程圖 18
圖4-1年齡遺漏值分布(填補前) 32
圖4-2年齡遺漏值分布(填補後) 33
圖4-3階層式集群分析樹狀圖 36
圖4-4輪廓係數 36
圖4-5四群之資料分布 37
圖4-6四群顧客之年齡分布 42
圖4-7決策樹結果 45
圖4-8決策樹混淆矩陣 47

表目錄
表2-1電子商務模式 6
表3-1資料檔案筆數 19
表3-2會員資料檔案變數說明 19
表3-3訂單主檔變數說明 20
表3-4訂購明細檔變數說明 20
表3-5三種決策樹演算法整理 25
表3-6混淆矩陣表 26
表4-1居住地區重新編碼 30
表4-2消費總額合併前(取10筆) 31
表4-3消費總額合併後(取10筆) 31
表4-4遺漏值統計-年齡 32
表4-5顧客採購清單(取10筆) 33
表4-6類別型基本資料統計 34
表4-7數值型資本資料統計 34
表4-8調整後RFM指標定義 35
表4-9會員RFM資料 35
表4-10四群之RFM 37
表4-11RFM指標之ANOVA結果 38
表4-12RFM指標之事後檢定結果 38
表4-13會員等級之卡方檢定結果 40
表4-14四群顧客類型之會員等級分布狀況 40
表4-15年齡之ANOVA結果 41
表4-16年齡之事後檢定結果 41
表4-17性別之卡方檢定結果 43
表4-18四群顧客之性別分布狀況 43
表4-19名單來源之卡方檢定結果 44
表4-20四群顧客類型之名單來源分布狀況 44
表4-21決策樹之模型指標 47
表4-22鮮肉顧客群之單項商品支持度 48
表4-23鮮肉顧客群之商品關聯規則 48
表4-24沉睡巨人顧客群之單項商品支持度 49
表4-25忠誠型顧客群之單項商品支持度 49
表4-26忠誠型顧客群之商品關聯規則 49
表4-27流失型顧客群之單項商品支持度 50
表4-28流失型顧客群之商品關聯規則 50

一、中文文獻
1.吳培聖(2013)。產品購物籃分析-以亞馬遜網站購物為例。國立暨南國際大學國際企業學系學位論文，南投縣。
2.李右婷(2020)。日本零售業於電子商務發展時代的人才培育發展之研究。東亞論壇，508，25-36。
3.林亭汝、蔡孟倫(2017)。巨量資料分析應用於電子商務企業經營模式之研究。價值管理期刊。27，13-22。
4.陳彥君(2021)。以決策樹與判別分析進行顧客分群：以A公司為例之RFM分析架構。政治大學企業管理研究所學位論文，台北市。
5.陳傑豪(2015)。大數據玩行銷。台北市：30雜誌出版。
6.趙永祥、潘偉華、周秀蓉(2005)。後WTO時代台灣地區銀行業電子商務發展與顧客關係管理策略之探討。建國科大學報，24(2)，169-192。
7.蔡瑞木(2013)。資料探勘之應用—以某百貨公司為例。臺北大學企業管理學系學位論文，台北市。
8.蕭錫年(2019)。產品購物籃分析-以某超商為例。國立雲林科技大學資訊管理系學位論文，雲林縣。
9.簡禎富、許嘉裕(2014)。資料挖礦與大數據分析。前程文化。
二、英文文獻
1.Aggarwal, A. G., & Yadav, S. (2020). Customer Segmentation Using Fuzzy-AHP and RFM Model. In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization, 9(8), 77-80.
2.Aktas, E., & Meng, Y. (2017). An exploration of big data practices in retail sector. Logistics, 1(2).
3.Alizadeh Zoeram, A., & Karimi Mazidi, A. R. (2018). New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System. Iranian Journal of Management Studies, 11(2), 351-378.
4.Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2018). Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes. Annals of Operations Research, 270(1-2), 25-51.
5.Alvandi, M., Fazli, S., & Abdoli, F. S. (2012). K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services. International Research Journal of Applied and Basic Sciences, 3(11), 2294-2302.
6.Amine, A., Bouikhalene, B., & Lbibb, R. (2015). Customer segmentation model in e-commerce using clustering techniques and LRFM model: The case of online stores in Morocco. International Journal of Computer and Information Engineering, 9(8), 2000-2010.
7.Amine, A., Bouikhalene, B., & Lbibb, R. (2015). Customer segmentation model in e-commerce using clustering techniques and LRFM model: The case of online stores in Morocco. International Journal of Computer and Information Engineering, 9(8), 2000-2010.
8.Anitha, P., & Patil, M. M. (2019). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University-Computer and Information Sciences, 51(2), 531-562.
9.Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398.
10.Baghernia, A., Pavin, H., Mirnabibaboli, M., & Alinejad-Rokny, H. (2017). Clustering high-dimensional data stream: A survey on subspace clustering, projected clustering on bioinformatics applications (advanced science, engineering and medicine, vol. 8(9), 749–757.
11.Balci, G., & Cetin, I. B. (2017). Market segmentation in container shipping services: A qualitative study: MRN. Management Research Review, 40(10), 1100-1116.
12.Banerjee, S., Choudhary, A., & Pal, S. (2015). Empirical evaluation of k-means, bisecting k-means, fuzzy c-means and genetic k-means clustering algorithms. In 2015 IEEE international WIE conference on electrical and computer engineering 30(5),168-172. 
13.Barbu, A., & Tiganoaia, B. (2018). Customer lifetime value and customer loyalty. Journal of Information Systems & Operations Management, 303-311.
14.Bicego, M. (2019). K-Random Forests: a K-means style algorithm for Random Forest clustering. In 2019 International Joint Conference on Neural Networks, IJCNN, 1-8.
15.Bohari, A. M., Rainis, R., & Marimuthu, M. (2012). Theoretical issues on contemporary methods of estimate the lifetime value of hypermarket business: geospatial approach as new method. Journal of Asian Business Strategy, 2(11), 250-273.
16.Boutsouki, C. (2019). Impulse behavior in economic crisis: A data driven market segmentation. International Journal of Retail & Distribution Management, 47(9), 974-996.
17.Bruwer, J., Roediger, B., & Herbst, F. (2017). Domain-specific market segmentation: A wine-related lifestyle (WRL) approach. Asia Pacific Journal of Marketing and Logistics, 29(1), 4-26.
18.Brzezinski, D., & Stefanowski, J. (2017). Prequential AUC: properties of the area under the ROC curve for data streams with concept drift. Knowledge and Information Systems, 52(2), 531-562.
19.Chang, C. C., & Chen, S. H. (2015). A comparative analysis on artificial neural network-based two-stage clustering. Cogent Engineering, 2(1), 785-797.
20.Chang, E. C., Huang, S. C., & Wu, H. H. (2010). Using K-means method and spectral clustering technique in an outfitter’s value analysis. Quality & Quantity, 44(4), 807-815.
21.Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management, 19(3), 197-208.
22.Chen, T. (2012). The RFM–FCM approach for customer clustering. International Journal of Technology Intelligence and Planning, 8(4), 358-373.
23.Cheng, J. (2013). Information Diffusion and Influence Propagation on Social Networks with Marketing Applications, 13(3), 428-453.
24.Chiang, W. Y. (2019). Establishing high value markets for data-driven customer relationship management systems. Kybernetes, 16(3), 351-370.
25.Chowdhury, A. R., Chatterjee, T., & Banerjee, S. (2019). A random forest classifier-based approach in the detection of abnormalities in the retina. Medical & biological engineering & computing, 57(1), 193-203.
26.Daoud, R. A., Amine, A., Bouikhalene, B., & Lbibb, R. (2015). Combining RFM model and clustering techniques for customer value analysis of a company selling online. In 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications , 1-6.
27.Das, M., Klier, J., Klier, M., Lindner, G., & Thiel, L. (2017). Customer lifetime network value: customer valuation in the context of network effects. Electronic Markets, 27(4), 307-328.
28.Dash, P., & Mishra, S. (2010). Developing RFM model for customer segmentation in retail industry. International Journal of Marketing & Human Resource Management (IJMHRM), 1(1), 58-69.
29.Deetz, M. (2018). K-Means Clustering Von Self-Organizing Maps: Eine Empirische Studie Zum Informationsgehalt Der Selbsteinstufung Von Hedge-Fonds (K-means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of the Self-Classification of Hedge Funds),28(2), 366-381.
30.Djafri, L., Bensaber, D. A., & Adjoudj, R. (2018). Big Data analytics for prediction: parallel processing of the big learning base with the possibility of improving the final result of the prediction. Information Discovery and Delivery, 36(3), 139-166.
31.Dogan, O., Ayçin, E., & Bulut, Z. A. (2018). Customer segmentation by using RFM model and clustering methods: a case study in retail industry. International Journal of Contemporary Economics and Administrative Sciences, 8(1), 1-19.
32.Dou, X. (2020). Online Purchase Behavior Prediction and Analysis Using Ensemble Learning. In 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics , 532-536.
33.Esteves, R. M., Hacker, T., & Rong, C. (2013). Competitive k-means, a new accurate and distributed k-means algorithm for large datasets. In 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, 17-24.
34.Ezhilarasan, C., & Ramani, S. (2017). Performance prediction using modified clustering techniques with fuzzy association rule mining approach for retail. In 2017 International Conference on Intelligent Computing and Control, 1-6.
35.Fix, E., Hodges, J.L. (1951). Discriminatory analysis, nonparametric discrimination:Consistency properties, Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas.
36.Frow, P., Nenonen, S., Payne, A., & Storbacka, K. (2015). Managing co‐creation design: A strategic approach to innovation. British Journal of Management, 26(3), 463-483.
37.Gan, G., & Valdez, E. A. (2020). Data clustering with actuarial applications. North American Actuarial Journal, 24(2), 168-186.
38.Giri, C., Johansson, U., & Löfström, T. (2019). Predictive Modeling of Campaigns to Quantify Performance in Fashion Retail Industry. In 2019 IEEE International Conference on Big Data, 2267-2273.
39.Guo, B., Song, L., Zheng, T., Liang, H., & Wang, H. (2019). A Comparative Evaluation of SOM-based Anomaly Detection Methods for Multivariate Data. In 2019 Prognostics and System Health Management Conference, 1-6.
40.Guo, Y., Wang, M., & Li, X. (2017). Application of an improved apriori algorithm in a mobile e-commerce recommendation system. Industrial Management & Data Systems, 117(2), 287-303.
41.Han, P., Shang, S., Sun, A., Zhao, P., Zheng, K., & Kalnis, P. (2019). AUC-MF: point of interest recommendation with AUC maximization. In 2019 IEEE 35th International Conference on Data Engineering, 1558-1561.
42.Hosseini, M., & Shabani, M. (2015). New approach to customer segmentation based on changes in customer value. Journal of Marketing Analytics, 3(3), 110-121.
43.Hu, Y., & Yeh, T. (2014). Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowledge-Based Systems, 61, 76-88.
44.Huai-bin, W., Hong-liang, Y., Zhi-Jian, X. U., & Zheng, Y. (2010). A clustering algorithm use SOM and k-means in intrusion detection. In 2010 International Conference on E-Business and E-Government, 1281-1284..
45.I-Fei, C., & Chi-Jie, L. (2017). Sales forecasting by combining clustering and machine-learning techniques for computer retailing. Neural Computing & Applications, 28(9), 2633-2647.
46.Jasek, P. (2015). Impact of Customer Networks on Customer Lifetime Value Models, European journal of business and social sciences, 5(2), 79-92
47.Jinguji, A., Sato, S., & Nakahara, H. (2018). An FPGA realization of a random forest with k-means clustering using a high-level synthesis design. IEICE TRANSACTIONS on Information and Systems, 101(2), 354-362.
48.Jinshu, L., Yijiang, W., Ganjun, W., Xiaoasheng, P., Taiwei, L., & Yuhang, J. (2018). Gradient Boosting Decision Tree and Random Forest Based Partial Discharge Pattern Recognition of HV Cable. In 2018 China International Conference on Electricity Distribution, 327-331.
49.Jose M. Jerez a, Ignacio Molina b , Pedro J. Garcı´a-Laencina c , Emilio Alba d ,Nuria Ribelles d , Miguel Martı´n e , Leonardo Franco. (2010). Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine, 50, 105–115.
50.Joshi, R., Gupte, R., & Saravanan, P. (2018). A random forest approach for predicting online buying behavior of Indian customers. Theoretical Economics Letters, 8(03), 448.
51.Jung, C. H., & Jung, D. H. (2016). The Effects of M-CRM Characteristics, Market Orientation on Customer Loyalty and the Moderating Role of Relationship Length in Insurance Companies. The Journal of the Korea Contents Association, 16(6), 726-738.
52.Kashwan, K. R., & Velu, C. M. (2013). Customer segmentation using clustering and data mining techniques. International Journal of Computer Theory and Engineering, 5(6), 856.
53.Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
54.Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63.
55.Khandpur, N., Zatz, L. Y., Bleich, S. N., Taillie, L. S., Orr, J. A., Rimm, E. B., & Moran, A. J. (2020). Supermarkets in cyberspace: A conceptual framework to capture the influence of online food retail environments on consumer behavior. International journal of environmental research and public health, 17(22), 8639.
56.Krstevski, D., & Mancheski, G. (2016). Managerial accounting: Modeling customer lifetime value-An application in the telecommunication industry. European journal of business and social sciences, 5(1), 64-77.
57.Kumar, S., Eidem, J., & Diana, N. P. (2012). Clash of the e-commerce titans. International Journal of Productivity and Performance Management, 61(7), 805-830.
58.Kusrini, K. (2015). Grouping of Retail items by using K-Means clustering. Procedia Computer Science, 72, 495-502.
59.Ladyzynski, P., Zbikowski, K., & Gawrysiak, P. (2019). Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Systems with Applications, 134, 28-35.
60.Lalou, P., Ponis, S. T., & Efthymiou, O. K. (2020). Demand forecasting of retail sales using data analytics and statistical programming. Management & Marketing, 15(2), 186-202.
61.Li, D. C., Dai, W. L., & Tseng, W. T. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems with Applications, 38(6), 7186-7191.
62.Li, J., He, X., Cai, Y., & Xu, Q. (2017). Method of WiFi indoor location based on K-means and Random Forest. Control. Eng. China, 24, 787-792.
63.Lingqing, G., Xiaobin, C., Zhaoming, L., Jinping, K., Bingchen, L., & Sha, L. (2019). Detection Method for Power Theft Based on SOM Neural Network and K-means Clustering Algorithm. In 2019 22nd International Conference on Electrical Machines and Systems, 1-5.
64.Liu, D., & Sun, K. (2019). Random forest solar power forecast based on classification optimization. Energy, 187, 115940.
65.Liu, Y., Tang, Z., & Zheng, W. (2019). Suspicious Bank Card Transaction Recognition Based on K-means Clustering and Random Forest Algorithm. In 2019 International Conference on Intelligent Informatics and Biomedical Sciences, 332-336.
66.Loureiro, A. L., Miguéis, V. L., & da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93.
67.Manero, K. M., Rimiru, R., & Otieno, C. (2018). Customer behaviour segmentation among mobile service providers in kenya using K-means algorithm. International Journal of Computer Science Issues (IJCSI), 15(5), 67-76.
68.Manikandan, P. (2018). Medical Big Data Classification Using a Combination of Random Forest Classifier and K-Means Clustering. International Journal of Intelligent Systems and Applications, 10(11), 11-47.
69.Marisa, F., Ahmad, S. S. S., Yusof, Z. I. M., Hunaini, F., & Aziz, T. M. A. (2019). Segmentation model of customer lifetime value in small and medium enterprise (SMEs) using K-means clustering and LRFM model. International Journal of Integrated Engineering, 11(3), 221-268.
70.Martinez-Pabon, M., Eveleigh, T., & Tanju, B. (2017). Smart Meter Data Analytics for Optimal Customer Selection in Demand Response Programs. Energy Procedia, 100(107), 49-59.
71.Mauricio, A. P., Payawal, J. M. M., Cueva, M. A. D., & Quevedo, V. C. (2016). Predicting customer lifetime value through data mining technique in a direct selling company. In 2016 International Conference on Industrial Engineering, Management Science and Application , 1-5.
72.Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67-72.
73.Monalisa, S. (2018). Klasterisasi Customer Lifetime Value Dengan Model LRFM Menggunakan Algoritma K-Means. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 5(2), 247-252.
74.Murlidhar, V., Menezes, B., Sathe, M., & Murlidhar, G. (2012). A Clustering Based Forecast Engine for Retail Sales. J. Digit. Inf. Manag., 10(4), 219-229.
75.Padhye, G., & Sangvikar, B. V. (2016). Understanding the Repatronage Intentions of Supermarket Customers: A Cluster Analysis. IUP Journal of Marketing Management, 15(2), 195-237.
76.Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: A case study. Marketing Intelligence & Planning, 35(4), 544-559.
77.Pramono, P. P., Surjandari, I., & Laoh, E. (2019). Estimating Customer Segmentation based on Customer Lifetime Value Using Two-Stage Clustering Method. In 2019 16th International Conference on Service Systems and Service Management, 1-5.
78.Qi, J. Y., Zhou, Y. P., Chen, W. J., & Qu, Q. X. (2012). Are customer satisfaction and customer loyalty drivers of customer lifetime value in mobile data services: a comparative cross-country study. Information Technology and Management, 13(4), 281-296.
79.Rachid, A. D., Abdellah, A., Belaid, B., & Rachid, L. (2018). Clustering prediction techniques in defining and predicting customers defection: The case of e-commerce context. International Journal of Electrical and Computer Engineering, 8(4), 23-67.
80.Ragazzo, C., & Monteiro, G. (2018). Big data e concorrência: Quando big data é uma variável competitiva em mercados digitais e deve ser considerada na análise concorrencial? Economic Analysis of Law Review, 9(3), 150-177.
81.Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of marketing, 64(4), 17-35.
82.Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 34(4), 446-461.
83.Sheikh, A., Ghanbarpour, T., & Gholamiangonabadi, D. (2019). A preliminary study of fintech industry: A two-stage clustering analysis for customer segmentation in the B2B setting. Journal of Business-to-Business Marketing, 26(2), 197-207.
84.Shen, Z., Feng, B., & Li, Y. (2020). Research of Product Data Measurement Mode Based on Neural Network. In 2020 3rd International Conference on Electron Device and Mechanical Engineering, 581-586.
85.Shukla, M. K., & Pattnaik, P. N. (2019). Managing customer relations in a modern business environment: Towards an ecosystem-based sustainable CRM model. Journal of Relationship Marketing, 18(1), 17-33.
86.Steel, M., Dubelaar, C., & Ewing, M. T. (2013). Developing customised CRM projects: The role of industry norms, organisational context and customer expectations on CRM implementation. Industrial Marketing Management, 42(8), 1328.
87.Stone B. (1995). Successful Direct Marketing Methods. NTC Business Books, Lincolnwood.
88.Strycharz, J., Guda, v. N., Helberger, N., & Smit, E. (2019). Contrasting perspectives – practitioner’s viewpoint on personalised marketing communication. European Journal of Marketing, 53(4), 635-660.
89.Tian, X., & Liu, L. (2017). Does big data mean big knowledge? integration of big data analysis and conceptual model for social commerce research. Electronic Commerce Research, 17(1), 169-183.
90.Tripathi, N., Vartak, D., Chaudhari, H., & Naik, S. (2018). Estimating Frequent Products in Shopping Cart Using Data Mining. In 2018 Second International Conference on Inventive Communication and Computational Technologies, 1560-1564.
91.Vasumathi, M. T., & Kamarasan, M. (2019). A Comparative Study on Traditional Data Mining and Big Data Mining Classification Algorithms. Journal of Computational and Theoretical Nanoscience, 16(4), 1523-1527.
92.Wang, J., & Fan, X. (2019). Co-production strategy, retail competition, and market segmentation. Asia Pacific Journal of Marketing and Logistics, 31(2), 607-630.
93.Wang, M. C., & Li, S. (2013). ROC analysis for multiple markers with tree-based classification. In Risk Assessment and Evaluation of Predictions , 21(1)179-198.
94.Wei, J. T., Lin, S. Y., Weng, C. C., & Wu, H. H. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Applications, 39(5), 5529-5533.
95.Westland, J. C., Mou, J., & Yin, D. (2019). Demand cycles and market segmentation in bicycle sharing. Information Processing & Management, 56(4), 1592-1604.
96.Wielki, J. (2020). Analysis of the role of digital influencers and their impact on the functioning of the contemporary on-line promotional system and its sustainable development. Sustainability, 12(17), 7138.
97.Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg. (2008). Top 10 algorithms in data mining, Knowl Inf Syst, 14, 1–37.
98.Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2017). E-commerce logistics in supply chain management: Implementations and future perspective in furniture industry. Industrial Management & Data Systems, 117(10), 2263-2286
99.Zalaghi, Z., & Varzi, Y. (2014). Measuring customer loyalty using an extended RFM and clustering technique. Management Science Letters, 4(5), 905-912.