電子商務會員分群與消費行為關聯分析 以AL服飾公司為例

Association Analysis between Membership Segmentation and E-commerce Consumer Behavior A Case Study of AL Apparel Company

陳宇鼎(Yu-Ting Chen)

指導教授 ： 藍俊宏

國立臺灣大學/工學院/工業工程學研究所/碩士(2024年)

https://doi.org/10.6342/NTU202402689

摘要

隨著電子商務的迅速發展和資料分析技術的進步，企業能夠更深入地理解消費者行為，從而實施更有效的市場分群和個性化營銷策略。本研究以AL服飾公司為例，探討了電子商務平台會員的可能分群方式及其消費行為的關聯性。研究採用量化分析架構，以會員的購買記錄、瀏覽偏好和互動記錄為基礎，透過K-Means分群算法來識別不同的消費者群體。
研究結果顯示，平台會員可被劃分為數個明顯不同的群體，這些群體在購買頻率、喜好品類和價格敏感度等方面具有顯著差異。基於這樣的分群，本研究提出了針對不同消費者群體的個性化行銷策略，旨在提升顧客滿意度和忠誠度，並促進銷售業績的增長。最後，本研究討論了分群結果對於電子商務策略制定的實務意義，並提出了未來研究的方向，如進一步深化分群的細節，以及整合其他潛在的資料來源以深入分析交易行為等。

關鍵字

電子商務 ； 消費者行為 ； 分群分析 ； K-Means演算法 ； 個性化行銷

並列摘要

With the rapid development of e-commerce and technological advancements in data analytics, businesses gain deeper insights into consumer behavior, thereby implementing more effective segmentation and personalized marketing strategies. This study takes AL Apparel Company as a case example to explore potential segmentation methods for members on e-commerce platforms and their associated consumer behaviors. The research employs a quantitative analysis framework, using purchasing records, browsing preferences, and interaction logs as a basis for identifying different consumer groups through the K-Means clustering algorithm.
The results indicate that platform members can be divided into several distinct groups, which exhibit significant differences in purchase frequency, preferred categories, and price sensitivity. Based on these segments, the study proposes personalized marketing strategies tailored to different consumer groups aimed at enhancing customer satisfaction and loyalty and fostering sales performance growth. Lastly, the study discusses the practical implications of the segmentation results for e-commerce strategy development and proposes directions for future research, such as further refining the segmentation details and integrating other potential data sources to analyze transaction behaviors more deeply.

並列關鍵字

eCommerce ； Consumer Behavior ； Clustering Analysis ； K-Means Algorithm ； Market Segmentation ； Personalized Marketing

參考文獻

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