Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/157791
|
Title: | 購物籃分析於保養品電商之行銷應用 The Application of Market Basket Analysis in Skincare E-Commerce |
Authors: | 温之楷 Wen, Chih-Kai |
Contributors: | 胡昌亞 莊皓鈞 Hu, Chang-Ya Chuang, Hao-Chun 温之楷 Wen, Chih-Kai |
Keywords: | 購物籃分析 關聯規則 電子商務 Market Basket Analysis Association Rules E-Commerce |
Date: | 2025 |
Issue Date: | 2025-07-01 14:57:09 (UTC+8) |
Abstract: | 在大數據蓬勃發展的時代下,企業運用大量交易紀錄進行消費者行為分析,以應用於銷售策略發展。而購物籃分析 (market basket analysis, MBA),又稱關聯性分析 (association) 是根據單次交易紀錄中經常共同出現的商品組合頻率,找出可能會一起購買的商品組合,進而找出可能的綑綁銷售產品組合。本研究以保養品電子商務B品牌為例,從11,827個會員交易購物車對33種商品進行購物籃分析。分析結果指出,個案品牌多數消費者於單筆消費中僅會選擇一項商品,且商品偏好高度集中於特定商品。未來銷售策略方向可著重宣傳明星商品之高關聯商品的搭配使用效果,提升消費者購買多品項的意願。 Under rapid big data development, enterprises utilize large volumes of transaction records to analyze consumer behavior and apply the insights to sales strategy development. Market Basket Analysis (MBA), also known as Association Rule Analysis, identifies product combinations that are frequently purchased together within a single transaction, thereby uncovering potential bundled product groupings. This study takes a skincare e-commerce company, referred to as Brand B, as the research case. A Market Basket Analysis was conducted using 11827 member shopping cart transactions involving 33 types of products. The analysis results indicate that most consumers of the case brand tend to purchase only one item per transaction, and product preferences are highly concentrated. Future sales strategies may focus on promoting the combined use of highly associated products with popular items to increase consumers’ willingness to purchase multiple items in a single transaction. |
Reference: | 一、中文文獻 王素彎 (2023)。電子商務成長可期,統計資訊需求日增。經濟前瞻,208,136–140。https://www.airitilibrary.com/Article/Detail?DocID=10190376-N202307200007-00022 天下雜誌 (2024年3月15日)。台北富邦銀行攜手 HPE,打造全台首套 AI 鷹眼識詐金融防護網。取自 https://www.cw.com.tw/article/5129619 天新資訊 (2021年6月9日)。消費者的購前行為你掌握了嗎?破解品牌經營的兩大要素。取自https://www.fiti.com.tw/blog/personalized_marketing/ 李月華、王盡忠 (2017)。選擇性組合之品牌及價格配適對消費者選擇行為之影響。輔仁管理評論,24(2),19–44。https://www.airitilibrary.com/Article/Detail?DocID=10254412-201705-201707110012-201707110012-19-44 翁慈宗 (2009)。資料探勘的發展與挑戰。科學發展月刊,442,32–39。 陳詩雅、楊玉奇 (2011)。消費者線上瀏覽對網路商品購買行為影響之研究。 輔仁管理評論,18(3),49–74。https://doi.org/10.29698/FJMR.201109.0003 連志峰 (2021)。消費者網路購物行為分析之研究。計量管理期刊,18(1),123–134。https://www.airitilibrary.com/Article/Detail?DocID=P20201105001-202105-202104120021-202104120021-123-134 莊凱傑 (2017)。網路購物平台應用大數據分析擬定行銷決策之研究—以國內電商為例〔未出版之碩士論文〕。東吳大學國際經營與貿易學系。https://hdl.handle.net/11296/5jz59u 郭瀚揚 (2019)。資料探勘應用之研究:零售業的 RFM 分析架構〔未出版之碩士論文〕。國立臺灣師範大學全球經營與策略研究所。https://hdl.handle.net/11296/nru7uf 黃博威 (2012)。台灣電子商務公司經營模式分析〔未出版之碩士論文〕。國立中山大學企業管理學系研究所。https://hdl.handle.net/11296/hv9qft 楊書豪 (2021)。教育大數據:資料探勘技術之應用-以彰化某國中為例〔未出版之碩士論文〕。國立彰化師範大學資訊工程學系。https://hdl.handle.net/11296/9qyxa7 經濟部 (2024年12月27日)。當前經濟情勢概況 (專題:實體零售業與電子購物業近年網路銷售發展趨勢分析)。取自 https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=23&html=1&menu_id=10212&bull_id=16541 鄭睿合、王素彎 (2023)。臺灣電商市場規模推估與市場界定省思。經濟前瞻,209,56–60。https://www.airitilibrary.com/Article/Detail?DocID=10190376-N202309230008-00009 臺灣傳播調查資料庫 (n.d.)。你又網購了嗎?-台灣民眾網路購物之情形。檢索日期為2025年3月12日,取自 https://crctaiwan.dcat.nycu.edu.tw/ResultsShow_detail.asp?RS_ID=149 歐洲在台商務協會 (2020年10月28日)。E-commerce in Taiwan. 取自 https://www.ecct.com.tw/e-commerce-in-taiwan/ 劉尊云 (2023)。D2C行銷模式對顧客購買意願影響之研究—以購買 Nike 商品為例〔未出版之碩士論文〕。銘傳大學新媒體暨傳播管理學系。https://hdl.handle.net/11296/ab86st 謝邦昌、陳銘芷 (2017)。大數據下多變量應用分析。元華文創。 Amazon (2023年6月1日)。Amazon 品牌分析手冊。取自https://m.mediaamazon.com/images/G/28/TWGS/SU/Amazon_Brand_Analytics_Playbook_TW.pdf
二、英文文獻 Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Conference, 207–216. https://doi.org/10.1145/170036.170072 Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499. Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology. arXiv. https://arxiv.org/abs/1901.04028 Bruno, P., & Melnyk, V. (2019). Analysis of shopping baskets: Insights into customer behavior. European Journal of Marketing, 53(4), 742-767. https://doi.org/10.1108/EJM-06-2017-0367 Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37. https://doi.org/10.1609/aimag.v17i3.1230 Joe, T., Sreejith, R., & Sekar, K. (2019). Optimization of store layout using market basket analysis. International Journal of Recent Technology and Engineering, 8(2), 3947–3950. https://www.ijrte.org/wp-content/uploads/papers/v8i2/B2207078219.pdf Musalem, A., Aburto, L., & Bosch, M. (2018). Market basket analysis insights to support category management. European Journal of Marketing, 52(7/8), 1550–1573. https://doi.org/10.1108/EJM-06-2017-0367 Sreenivasan, L., Iyer, G., Pradhan, M., & Abraham, P. (2016). Customer Analytics at Bigbasket - Product Recommendations. Harvard Business Review. https://www.researchgate.net/publication/316968828_Customer_Analytics_at_Bigbasket_-_Product_Recommendations Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Pearson. |
Description: | 碩士 國立政治大學 企業管理研究所(MBA學位學程) 112363028 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112363028 |
Data Type: | thesis |
Appears in Collections: | [企業管理研究所(MBA學位學程)] 學位論文
|
Files in This Item:
File |
Description |
Size | Format | |
302801.pdf | | 1532Kb | Adobe PDF | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|