學術產出-Theses
Article View/Open
Publication Export
-
題名 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作者 江冠駒
Chiang, Kuan-Chu貢獻者 周珮婷<br>林怡伶
Chou, Pei-Ting<br>Lin, Yi-Ling
江冠駒
Chiang, Kuan-Chu關鍵詞 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日期 2020 上傳時間 2-Sep-2020 11:42:13 (UTC+8) 摘要 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.參考文獻 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. 描述 碩士
國立政治大學
統計學系
107354006資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107354006 資料類型 thesis dc.contributor.advisor 周珮婷<br>林怡伶 zh_TW dc.contributor.advisor Chou, Pei-Ting<br>Lin, Yi-Ling en_US dc.contributor.author (Authors) 江冠駒 zh_TW dc.contributor.author (Authors) Chiang, Kuan-Chu en_US dc.creator (作者) 江冠駒 zh_TW dc.creator (作者) Chiang, Kuan-Chu en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:42:13 (UTC+8) - dc.date.available 2-Sep-2020 11:42:13 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:42:13 (UTC+8) - dc.identifier (Other Identifiers) G0107354006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131473 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) 107354006 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景與動機 11.2 研究目的 2第二章 文獻探討 32.1 電商值播 32.2 RFM模型 42.3 階層式分群 4第三章 研究方法 63.1 資料介紹與預處理 63.1.1 資料介紹 63.1.2 資料清理與前處理 73.2 演算法與模型介紹 83.2.1 階層式分群(Hierarchical Clustering, HC) 83.2.2 主成分分析(Principal Component Analysis, PCA) 103.2.3 RFM模型 113.2.4 回購率(Repurchase rate) 12第四章 研究結果 134.1 描述性統計 134.2 PCA 174.3 分群 184.4 群的特性介紹 194.5 RFM模型 324.6 回購率 33第五章 結論與未來展望 355.1 結論 355.2 未來展望 36第六章 參考資料 38 zh_TW dc.format.extent 2155371 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107354006 en_US dc.subject (關鍵詞) 階層式分群 zh_TW dc.subject (關鍵詞) RFM模型 zh_TW dc.subject (關鍵詞) 回購率 zh_TW dc.subject (關鍵詞) 電商直播 zh_TW dc.subject (關鍵詞) hierarchical clustering en_US dc.subject (關鍵詞) RFM model en_US dc.subject (關鍵詞) repurchase rate en_US dc.subject (關鍵詞) e-commerce live streaming en_US dc.title (題名) 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 zh_TW dc.title (題名) Research on e-commerce live streaming sellers` sales strategy using hierarchical clustering and RFM Analysis en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001179 en_US
