3C賣場POS交易資料於客戶關係管理應用之研究

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姓名

林祖全(Tsu-Chuan Lin) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

3C賣場POS交易資料於客戶關係管理應用之研究
(Application of 3C Product Retail Store Point of Sale Transaction Data in Customer Relationship Management)

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摘要(中)

隨著資訊科技發展日新月異，3C產品對於普羅大眾而言已是生活中不可缺少的一部分，消費者對於3C產品方面的需求也有一定程度。面對消費者在消費行為方面已有有愈來愈多樣化的表現的情況下，企業在行銷策略上的布局若只仰賴單一的行銷手法已經無法滿足現今時下消費者的需求。因此台灣各家3C賣場通路無不積極發展多樣化的消費管道、獨特的顧客關係管理策略以搶佔3C零售市場大餅。
而零售業實體通路的POS交易資料就成為企業能妥善利用的資源，若能應用資訊探勘的技術於顧客交易資料上，做出顧客區隔，找出關鍵顧客群並投以精準行銷，將能提升顧客留存率同時培養顧客忠誠，創造更多的利潤收入。
因此本研究欲應用台灣某3C賣場之POS交易資料於顧客關係管理，首先將顧客的交易資料以RFM模型為基礎發展出七項顧客交易行為變數，並以這七項變數作為顧客分群的依據，藉由K-means演算法分群顧客為已流失顧客群、VIP顧客群、一般顧客群與有流失風險的一般顧客群，最後針對一般顧客群做出商品推薦。期望提升顧客未來的消費頻率與花費金額，為個案公司創造更多利潤收入。

關鍵字：3C賣場、RFM、顧客分群、推薦系統、協同過濾

摘要(英)

With the rapid development of information technology, 3C products have become an indispensable part of life for the general public, and consumers also have a certain degree of demand for 3C products. Since consumers’ consumption behavior has become more and more diversified, it is insufficient to satisfy consumers if enterprises only rely on a single marketing approach. Therefore, every 3C product retailers in Taiwan actively develop diversified consumption channels and unique customer relationship management strategies to seize the chance of expanding their territory in the 3C retail market.
POS transaction data in retail channels becomes a valuable resource that enterprises can make proper use of. If information exploration technology can be applied to customer transaction data to conduct customer segmentation, identify key customer groups and implement customized marketing strategy, it will help improve customer retention rate, cultivate customer loyalty and create more profits .
This study utilizes POS transaction data of a Taiwan 3C retail store to conduct customer relationship management. First, develop seven customer consuming behavior related variables base on RFM model, using theses seven variables as the basis of the customer segmentation. Then, using K-means algorithm to implement customer segmentation and divide customers into lost customers, VIP customers, regular customers and regular customers who are at risk of loss. Finally, choosing regular customers as target segment to make product recommendation. It is expected to increase the consumption frequency and the spending amount of the customers, eventually help create more profit for the 3C retail store in this case.

Keyword: 3C Retail, RFM, Customer Segmentation, Recommender System, Collaborative Filtering

關鍵字(中)

★ 3C賣場
★ RFM
★ 顧客分群
★ 推薦系統
★ 協同過濾

關鍵字(英)

★ 3C Retail
★ RFM
★ Customer Segmentation
★ Recommender System
★ Collaborative Filtering

論文目次

中文摘要 i
ABSTRACT ii
目錄 iii
圖片目錄 v
表格目錄 vi
第一章緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 3
1-4 論文架構 4
第二章文獻探討 5
2-1 顧客關係管理 5
2-2 顧客分群 6
2-3 RFM模型 7
2-4 分群分析與演算法 8
2-5 推薦系統 13
第三章研究方法 15
3-1 研究流程架構 15
3-2 研究變數說明 16
3-3 資料來源與預處理 16
3-4 資料探勘方法 18
第四章研究結果 22
4-1 會員顧客分群分析結果 22
4-2 影響各分群分類規則之重要變數篩選 24
4-3 分群命名與目標分群選定 25
4-4 群集三一般顧客群商品推薦 27
4-5 群集四有流失風險顧客群商品推薦 30
第五章結論與未來研究建議 33
5-1 研究結論 33
5-2 研究限制與未來研究建議 33
參考文獻 34

參考文獻

Beijerse, R.P. (1999), “Questions in KM: defining and conceptualising a phenomenon”, Journal of Knowledge Management, Vol. 3 No. 2, pp. 94-109.
Abid, A., Kachouri, A., & Mahfoudhi, A. (2017). Outlier detection for wireless sensor networks using density-based clustering approach. IET Wireless Sensor Systems, 7(4), 83-90.
Aggarwal, C. C. (2016). Recommender systems (Vol. 1): Springer.
Ahearne, M., Rapp, A., Mariadoss, B. J., & Ganesan, S. (2012). Challenges of CRM implementation in business-to-business markets: A contingency perspective. Journal of Personal Selling & Sales Management, 32(1), 117-129.
Bass, F. M., Tigert, D. J., & Lonsdale, R. T. (1968). Market segmentation: Group versus individual behavior. Journal of Marketing Research, 5(3), 264-270.
Bonoma, T. V., & Shapiro, B. P. (1984). Evaluating market segmentation approaches. Industrial Marketing Management, 13(4), 257-268.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees: CRC press.
Chen, Y.-L., & Hung, L. T.-H. (2009). Using decision trees to summarize associative classification rules. Expert systems with applications, 36(2), 2338-2351.
Choi, K., Yoo, D., Kim, G., & Suh, Y. (2012). A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis. electronic commerce research and applications, 11(4), 309-317.
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. Paper presented at the 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA).
Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007). Google news personalization: scalable online collaborative filtering. Paper presented at the Proceedings of the 16th international conference on World Wide Web.
Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert systems with applications, 40(10), 3970-3983.
DeSarbo, W. S., Atalay, A. S., LeBaron, D., & Blanchard, S. J. (2008). Estimating multiple consumer segment ideal points from context-dependent survey data. Journal of Consumer Research, 35(1), 142-153.
Dibb, S. (1998). Market segmentation: strategies for success. Marketing Intelligence & Planning.
Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2011). Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 4(2), 81-173.
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Paper presented at the Kdd.
Fang, K., Jiang, Y., & Song, M. (2016). Customer profitability forecasting using Big Data analytics: A case study of the insurance industry. Computers & Industrial Engineering, 101, 554-564.
Fish, K. E., Barnes, J. H., & AikenAssistant, M. W. (1995). Artificial neural networks: a new methodology for industrial market segmentation. Industrial Marketing Management, 24(5), 431-438.
Freitas, A. A. (2003). A survey of evolutionary algorithms for data mining and knowledge discovery. In Advances in evolutionary computing (pp. 819-845): Springer.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques: Elsevier.
Hannon, J., Bennett, M., & Smyth, B. (2010). Recommending twitter users to follow using content and collaborative filtering approaches. Paper presented at the Proceedings of the fourth ACM conference on Recommender systems.
Hennig-Thurau, T., Gwinner, K. P., & Gremler, D. D. (2002). Understanding relationship marketing outcomes: an integration of relational benefits and relationship quality. Journal of service research, 4(3), 230-247.
Hiziroglu, A. (2013). Soft computing applications in customer segmentation: State-of-art review and critique. Expert systems with applications, 40(16), 6491-6507.
Hughes, A. M. (1994). Strategic database marketing: the masterplan for starting and managing a profitable. Customer-based Marketing Program, Irwin Professional.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Khalili-Damghani, K., Abdi, F., & Abolmakarem, S. (2018). Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries. Applied Soft Computing, 73, 816-828.
Khan, K., Rehman, S. U., Aziz, K., Fong, S., & Sarasvady, S. (2014). DBSCAN: Past, present and future. Paper presented at the The fifth international conference on the applications of digital information and web technologies (ICADIWT 2014).
Kim, J., & Lee, E. (2005). User XQuery pattern method based personalization recommender service. Paper presented at the 2005 First International Conference on Semantics, Knowledge and Grid.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), 90-95.
Kontoleon, A., & Yabe, M. (2006). Market segmentation analysis of preferences for GM derived animal foods in the UK. Journal of Agricultural & Food Industrial Organization, 4(1).
Kriegel, H. P., Kröger, P., Sander, J., & Zimek, A. (2011). Density‐based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 231-240.
Kuo, R., Ho, L., & Hu, C. M. (2002). Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers & Operations Research, 29(11), 1475-1493.
Lam, S. K., & Riedl, J. (2004). Shilling recommender systems for fun and profit. Paper presented at the Proceedings of the 13th international conference on World Wide Web.
Li, W., Wu, X., Sun, Y., & Zhang, Q. (2010). Credit card customer segmentation and target marketing based on data mining. Paper presented at the 2010 International Conference on Computational Intelligence and Security.
Liao, S.-h. (2003). Knowledge management technologies and applications—literature review from 1995 to 2002. Expert systems with applications, 25(2), 155-164.
Lourenço, A., Silva, H., & Carreiras, C. (2013). Outlier detection in non-intrusive ECG biometric system. Paper presented at the International Conference Image Analysis and Recognition.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Marakas, G. M. (2003). Decision support systems in the 21st century (Vol. 134): Prentice Hall Upper Saddle River, NJ.
Mazanec, J. A. (1992). Classifying tourists into market segments: A neural network approach. Journal of Travel & Tourism Marketing, 1(1), 39-60.
Mendoza, L. E., Marius, A., Pérez, M., & Grimán, A. C. (2007). Critical success factors for a customer relationship management strategy. Information and software technology, 49(8), 913-945.
Mitra, S., Pal, S. K., & Mitra, P. (2002). Data mining in soft computing framework: a survey. IEEE transactions on neural networks, 13(1), 3-14.
Ng, A. (2012). Clustering with the k-means algorithm. Machine Learning.
Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36(2), 2592-2602.
Pan, S. L., & Lee, J.-N. (2003). Using e-CRM for a unified view of the customer. Communications of the ACM, 46(4), 95-99.
Pandya, R., & Pandya, J. (2015). C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117(16), 18-21.
Sánchez-Moreno, D., González, A. B. G., Vicente, M. D. M., Batista, V. F. L., & García, M. N. M. (2016). A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert systems with applications, 66, 234-244.
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 1-21.
Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. E. (2001). Knowledge management and data mining for marketing. Decision support systems, 31(1), 127-137.
Siriprasoetsin, P., Tuamsuk, K., & Vongprasert, C. (2011). Factors affecting customer relationship management practices in Thai academic libraries. The international information & library review, 43(4), 221-229.
Smith, G., & Hirst, A. (2001). Strategic political segmentation-A new approach for a new era of political marketing. European Journal of Marketing, 35(9-10), 1058-1073.
Smith, K. A., Willis, R. J., & Brooks, M. (2000). An analysis of customer retention and insurance claim patterns using data mining: A case study. Journal of the operational research society, 51(5), 532-541.
Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of marketing, 21(1), 3-8.
Soltani, Z., & Navimipour, N. J. (2016). Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research. Computers in Human Behavior, 61, 667-688.
Suh, E., Noh, K., & Suh, C. (1999). Customer list segmentation using the combined response model. Expert systems with applications, 17(2), 89-97.
Thakur, R., & Workman, L. (2016). Customer portfolio management (CPM) for improved customer relationship management (CRM): Are your customers platinum, gold, silver, or bronze? Journal of Business Research, 69(10), 4095-4102.
Tsiotsou, R. (2006). Using visit frequency to segment ski resorts customers. Journal of Vacation Marketing, 12(1), 15-26.
Tyndale, P. (2002). A taxonomy of knowledge management software tools: origins and applications. Evaluation and program planning, 25(2), 183-190.
Vellido, A., Lisboa, P. J., & Vaughan, J. (1999). Neural networks in business: a survey of applications (1992–1998). Expert systems with applications, 17(1), 51-70.
Walter, F. E., Battiston, S., Yildirim, M., & Schweitzer, F. (2012). Moving recommender systems from on-line commerce to retail stores. Information Systems and e-Business Management, 10(3), 367-393.
Wang, C.-H. (2009). Outlier identification and market segmentation using kernel-based clustering techniques. Expert systems with applications, 36(2), 3744-3750.
Wedel, M., & Steenkamp, J.-B. E. (1989). A fuzzy clusterwise regression approach to benefit segmentation. International Journal of Research in Marketing, 6(4), 241-258.
Xia, J. C., Evans, F. H., Spilsbury, K., Ciesielski, V., Arrowsmith, C., & Wright, G. (2010). Market segments based on the dominant movement patterns of tourists. Tourism management, 31(4), 464-469.
Yang, A. X. (2004). How to develop new approaches to RFM segmentation. Journal of Targeting, Measurement and Analysis for Marketing, 13(1), 50-60.
Zakrzewska, D., & Murlewski, J. (2005). Clustering algorithms for bank customer segmentation. Paper presented at the 5th International Conference on Intelligent Systems Design and Applications (ISDA′05).
經濟部統計處(2018，7月5日).資通訊及家電設備零售業營業額恢復成長. 取自https://www.moea.gov.tw/mns/DOS/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=5266

指導教授

陳炫碩(Shiuann-Shuoh Chen)

審核日期

2020-7-29

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