政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131473

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131473

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政大典藏 > College of Commerce > Department of Statistics > Theses > Item 140.119/131473

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/131473

Title:	運用階層式分群與RFM分析於電商直播賣家銷售策略之研究 Research on e-commerce live streaming sellers` sales strategy using hierarchical clustering and RFM Analysis
Authors:	江冠駒 Chiang, Kuan-Chu
Contributors:	周珮婷林怡伶 Chou, Pei-Ting Lin, Yi-Ling 江冠駒 Chiang, Kuan-Chu
Keywords:	階層式分群 RFM模型回購率電商直播 hierarchical clustering RFM model repurchase rate e-commerce live streaming
Date:	2020
Issue Date:	2020-09-02 11:42:13 (UTC+8)
Abstract:	隨著網路時代的發達，線上購物蓬勃發展，根據經濟部統計處統計，電商佔整體零售業比重已經連續8年成長，其中近幾年以直播的方式銷售商品的賣家更是如雨後春筍般地出現，直播拍賣相較傳統的電商多了與顧客互動的即時性，吸引許多網拍業者投入，然而卻不是人人都能經營得成功。過去的相關研究都是針對顧客的特性進行分析，找出最有價值的客群；本研究透過電商直播訂單管理App所收集的賣家交易記錄，針對賣家的銷售行為模式進行分析，使用階層式分群(Hierarchical Clustering)演算法將賣家分成三群，並導入RFM模型，探討不同類型的賣家特徵，對於不同特性的直播主，找出在不同的時間點要賣哪類型的商品才能更有效率地獲利，提供賣家客製化的銷售策略。 With the development of the internet, online shopping has grown exponentially. According to the Department of Statistics of Ministry of Economic Affairs, the proportion of e-commerce in the overall retail industry has maintained 8 consecutive years of growth. Recently, there are more and more sellers selling their products through live streaming. In contrast to traditional e-commerce, sellers of live streaming auction can interact with customers in real-time. This feature attracts many vendors to try this new way to sell products, but not everyone can achieve success. In the past, relevant researches focus on the characteristics of customers to find out who is the most valuable customer. This research analyzes sales patterns of different sellers by RFM model and hierarchical clustering based on transaction records collected by an e-commerce live streaming order management App. For different kinds of live streaming vendors, find out which types of goods should be sold at different periods to earn most revenue, provide sellers customized sales strategies.
Reference:	一、中文參考文獻 Yulin（民106年11月17日）。【圖輯】臉書直播趨勢分析：人氣最高的不是美妝，而是賣運動鞋。The News Lens關鍵評論網。取自https://www.thenewslens.com/article/83459。何君豪（2006）。階層式分群法在民事裁判要旨分群上之應用。政治大學資訊科學學系學位論文。林瑞哲（2016）。以分群方法探討東亞國家貨幣整合的可行性。國立政治大學統計學系碩士論文。康云慈（2019）。探討電商直播對體驗價值及忠誠度之影響。輔仁大學企業管理學系管理學碩士在職專班碩士論文。郭瀚揚（2019）。資料探勘應用之研究:零售業的 RFM 分析架構。國立台灣師範大學全球經營與策略研究所碩士論文。傅少君（2017）。美妝電商網路購物行為分析-資料分群與關聯式規則之應用。國立臺北科技大學經營管理系碩士班碩士論文。嗎嘉應（2015）。運用階層式分群法及加權Apriori探討腦部健檢民眾回診之關聯法則。國立台灣科技大學工業管理學系碩士論文。經濟部統計處（民108年8月5日）。今年電子購物業營收可望再創新猷。取自https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=6182。資策會產業情報研究所（民108年6月3日）。【網購調查系列一】網購消費占比達16.5% 愛用電商平台大排名。取自https://mic.iii.org.tw/news.aspx?id=516&List=5。羅靚（2018）。電商直播的使用與滿足研究。政治大學傳播學院傳播碩士學位學程學位論文。二、英文文獻 Blashfield, R. K. (1976). Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods. Psychological Bulletin, 83(3), 377-388. Chen, C., Hu, Y., Lu, Y., & Hong, Y. (2019, January). Everyone can be a star: Quantifying grassroots online sellers’ live streaming effects on product sales. Paper present at the Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 2548-2557). Day, W. H., & Edelsbrunner, H. (1984). Efficient algorithms for agglomerative hierarchical clustering methods. Journal of classification, 1(1), 7-24. Gajewski, A. S. (2013). A qualitative study of how Facebook storefront retailers convert fans to buyers (Doctoral dissertation, Walden University). Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254. Kuiper, F. K. (1971). A Monte Carlo comparison of six clustering procedures (Doctoral dissertation, University of Washington). Leeraphong, A., & Sukrat, S. (2018, August). How Facebook Live Urge SNS Users to Buy Impulsively on C2C Social Commerce?. Paper presented at the Proceedings of the 2nd International Conference on E-Society, E-Education and E-Technology (pp. 68-72). Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The computer journal, 26(4), 354-359. Sneath, P. H., & Sokal, R. R. (1963). Principles of Numerical Taxonomy. San Franrcisco: W. H. Freeman. Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model. Iranian Accounting & Auditing Review, 14(47), 7-20. Sotiropoulos, D. N., Tzihrintzis, G. A., Savvopoulos, A., & Virvou, M. (2006, June). A comparison of customer data clustering techniques in an e-shopping Application. Paper presented at the Proceedings of 2nd International Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces. Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244. Wei, J. T., Lin, S. Y., & Wu, H. H. (2010). A review of the application of RFM model. African Journal of Business Management, 4(19), 4199-4206.
Description:	碩士國立政治大學統計學系 107354006
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107354006
Data Type:	thesis
DOI:	10.6814/NCCU202001179
Appears in Collections:	[Department of Statistics] Theses

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