系統識別號 U0026-2812202016284100
論文名稱(中文) 應用數據分析於零售業通路行銷之研究
論文名稱(英文) Applying Data Analysis to Retail Channel Marketing
校院名稱 成功大學
系所名稱(中) 會計學系
系所名稱(英) Department of Accountancy
學年度 109
學期 1
出版年 109
研究生(中文) 劉璦遠
研究生(英文) Ai-Yuan Liu
學號 R16074208
學位類別 碩士
語文別 中文
論文頁數 38頁
口試委員 指導教授-徐立群
中文關鍵字 沉睡顧客  喚醒行銷  商業智慧  RFM  精準行銷 
英文關鍵字 Sleep Customer  Evoking Marketing  BI  RFM  Precision Marketing 
中文摘要 對於任何一家企業來說,有一個重要的營運上之關鍵變數-顧客流失率,當一家公司的顧客流失率越高,也就表示這家公司有許多顧客跑到競爭對手的公司消費,或者存在著許多沉睡顧客。在各行各業中都有顧客流失的問題,通常企業都會試圖找出新顧客來取代流失顧客,但有研究指出更好的策略是喚醒這些沉睡已久的顧客。現今科技變遷迅速,公司內部資料量也越來越大,如何能從諸多資料中取得有效資訊,是目前許多公司都想得到的答案,而商業智慧就可以幫助公司有效的處理資訊並且協助管理階層做決策,像是顧客分群,顧客特徵、個人化推薦等。基於此,本研究提出一個通用喚醒行銷的篩選方法,將其應用到一家零售通路,藉由數據分析,找出該廠商的沉睡顧客。結果顯示,我們可以將一定數量有消費能力的沉睡顧客喚醒,符合廠商的需要。
英文摘要 There is a critical variable that is important to one of each enterprise - customer loss rate. The issue of losing the customer happens to all the industries. In general, the enterprise will try to locate the new customer as the replacement for lost customers. But some of the research indicates that wakes up the deep sleep customer will be considered a better strategy. Therefore, obtaining effective information from the vast database is the answer that all the companies want to achieve. Based on that, the study applied the data provided by a bedding retailer and proceed with the data analysis, and setting up three screening conditions for evoking marketing to locate the sleeping customer for the supplier. The screening conditions are the customer bears no transaction in a year-and-a-half, to dig out the customer with the potential purchasing power and the CAI value in the past must be excessive over zero. And to combine the customer's repurchase rate, the higher the possibility the early the priority to execute the marketing and promotion; the result indicates the screening condition in this study does fit into the supplier's characteristics and found the deep sleep customer with the purchasing power.
論文目次 第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 2
第二章 文獻探討 3
2.1 商業智慧(Business Intelligence) 3
2.1.1智慧零售 3
2.2 一般行銷 4
2.2.1 行銷定義 4
2.2.2 行銷理論演變 4
2.3 精準行銷(Precision Marketing) 6
2.3.1 精準行銷定義 6
2.3.2 RFM模型 6
2.4 喚醒行銷 9
2.4.1 顧客價值 10
2.4.2 顧客關係管理(Customer Relationship Management) 11
2.4.3 沉睡顧客 11
第三章 分析方法 14
3.1 流程架構 14
3.2 設計喚醒行銷之考量 15
3.2.1 商品週期 15
3.2.2 顧客消費能力 16
3.2.3 顧客活躍度 (Customer Active Index, CAI)分析 17
3.2.4結合精準行銷回購機率排名 20
3.3 商業智慧分析 21
第四章 實證結果 23
4.1 資料來源與描述型分析 23
4.2 喚醒行銷的條件及人數分布 26
4.3 實際測試結果及成效 28
4.4 顧客RFM分群 31
第五章 結論 33
5.1 結論 33
5.2 研究限制與建議 34
參考文獻 35
參考文獻 一、 中文部分
王曉鋒、張永強、吳笑一. (2015). 零售 4.0: 零售革命, 邁入虛實整合的全通路時代. 遠見天下文化出版股份有限公司。
吳奇廷. (2018). 應用機器學習於精準行銷之研究. 成功大學會計學系學位論文, (2018 年), pp 1-38。
呂惠聰、強南囡、王微微. (2018) .客戶關係管理(第三版) .台灣:財經錢線。
李奇樺、朱國光. (2012). 關係連結對挽回流失顧客影響之研究-以零售服務業為例. 管理學報, 29(1), pp 45-60。
陳傑豪. (2015). 大數據玩行銷.台灣:30雜誌。
趙滿鈴、江瑞清. (2017) .創新行銷與魅力品質:完整解構熱銷產品的行銷奧妙。台灣:台灣金融研訓院。
蔡宜霖. "應用機器學習於零售通路顧客之回購機率預測".成功大學會計研究所論文.研究進行中。
謝邦昌、蘇志雄、宋龍華. (2020) .Power BI零售大數據分析應用:強化工作效率,掌握市場先機!. 碁峰資訊股份有限公司。
羅元鴻. (2016). 購物籃與顧客活躍性分析:以線上購買飲食行為為例. 暨南大學國際企業學系學位論文, (2016 年), pp 1-53.
羅伯.馬凱. (2020). 哈佛商業評論(2020年2月號) 。台灣:鐵粉經濟學。
二、 英文部分
Aberdeen group (2006). How Top Performers Turbo-Charge Their Investments. The Precision Marketing Benchmark Report, pp3-4.
American Marketing Association. (1985). AMA board approves new marketing definition. Marketing News, 1(1).
Asllani, A., & Halstead, D. (2015). A multi-objective optimization approach using the RFM model in direct marketing. Academy of Marketing Studies Journal, 19(2), pp 65-79.
Brown, S. A., & Coopers, P. W. (1999). Customer relationship management: A strategic imperative in the world of e-business. John Wiley & Sons, Inc..
Day, G. S., & Wensley, R. (1983). Marketing theory with a strategic orientation. Journal of marketing, 47(4), pp 79-89.
González Martínez, R., Carrasco, R. A., García-Madariaga, J., Porcel Gallego, C. G., & Herrera Viedma, E. (2020). A comparison between Fuzzy Linguistic RFM Model and traditional RFM model applied to Campaign Management. Case study of retail business.
Greiner, D., & Kinni, T. B. (2011). 1,001 Ways to Keep Customers Coming Back: Wow Ideas that Make Customers Happy and Will Increase Your Bottom Line.
Griffin, J., & Lowenstein, M. W. (2002).Customer winback: How to recapture lost customers--And keep them loyal. John Wiley & Sons.
Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of interactive Marketing, 17(1), pp 9-24.
Homburg, C., Hoyer, W. D., & Stock, R. M. (2007). How to get lost customers back?. Journal of the Academy of Marketing Science, 35(4), pp461-474.
Hughes, Arthur M. (1994), Strategic database marketing. Probus Publishing Company, Chicago.
Jeffery, M. (2010). Data-driven marketing: the 15 metrics everyone in marketing should know. John Wiley & Sons.
John Oltman, Gresh Brebach and Russ Maney Seurat Company .(2002). “Precision Marketing: Optimizing Customer Profitability from a CEO’s Perspective”.
Kotler, P. (2004). Marketing redefined: Nine top marketers offer their personal definitions. Marketing News, 15(16).
Ku T., Chen PL., Yang PC. (2018) Sleeping Customer Detection Using Support Vector Machine. In: Hung J., Yen N., Hui L. (eds) Frontier Computing. FC 2017. Lecture Notes in Electrical Engineering, vol 464. Springer, Singapore
Kumar, V., Bhagwat, Y., & Zhang, X. (2015). Regaining “lost” customers: The predictive power of first-lifetime behavior, the reason for defection, and the nature of the win-back offer. Journal of Marketing, 79(4), pp34-55.
Lauterborn, B. (1990). New marketing litany: Four P’s passe. C-words take over.
Negash, S., & Gray, P. (2008). Business intelligence. In Handbook on decision support systems 2 (pp. 175-193). Springer, Berlin, Heidelberg.
Pantano, E. (2014). Innovation drivers in retail industry. International Journal of Information Management, 34(3), pp 344-350.
Pantano, E., & Viassone, M. (2015). Engaging consumers on new integrated multichannel retail settings: Challenges for retailers. Journal of Retailing and Consumer Services, 25, pp 106-114.
Reichheld, F. F., & Sasser, J. (1996). Zero defections: quality come to services, Harvard business review 68 (5), pp105-111.
Schmittlein, D. C., & Morrison, D. G. (1985). Is the customer still active?. The American Statistician, 39(4), pp291-295.
Stauss, B., & Friege, C. (1999). Regaining service customers: costs and benefits of regain management. Journal of Service Research, 1(4), pp347-361.
Strouse, K. G. (1999). Marketing telecommunications services: new approaches for a changing environment. Artech House Publishers.
Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies. Prentice Hall Professional.
Thomas, J. S., Blattberg, R. C., & Fox, E. J. (2010). Recapturing lost customers. In Perspectives On Promotion And Database Marketing: The Collected Works of Robert C Blattberg, pp. 229-243.
Tokman, M., Davis, L. M., & Lemon, K. N. (2007). The WOW factor: Creating value through win-back offers to reacquire lost customers. Journal of Retailing, 83(1), pp47-64.
Trieu, V. H. (2017). Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93, pp 111-124.
Turley, L. W., & Milliman, R. E. (2000). Atmospheric effects on shopping behavior: a review of the experimental evidence. Journal of business research, 49(2), pp 193-211.
Winer, R. S., & Neslin, S. A. (Eds.). (2014). The history of marketing science. New York, NY: World Scientific.
Wyner, G. A. (1996). Customer profitability: linking behavior to economics. Marketing Research, 8(2), pp36-38.
Zabin, J., & Brebach, G. (2004). Precision marketing: the new rules for attracting, retaining, and leveraging profitable customers. John Wiley & Sons.
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