電商會員分群之數位行銷實證研究;An Empirical Study of Digital Marketing through E-commerce Customer Segmentation

NCUIR > School of Management at National Central University > Executive Master of Information Management > Electronic Thesis & Dissertation > Item 987654321/93150

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93150

Title:	電商會員分群之數位行銷實證研究;An Empirical Study of Digital Marketing through E-commerce Customer Segmentation
Authors:	葉哲丞;Yeh, Che-Cheng
Contributors:	資訊管理學系在職專班
Keywords:	會員分群模型;顧客資料探勘;數位廣告;數位行銷;價格需求函數;RFM Model;Customer Data Mining;Digital Advertising;Digital Marketing;Price-Demand Function
Date:	2023-07-06
Issue Date:	2024-09-19 16:44:35 (UTC+8)
Publisher:	國立中央大學
Abstract:	因網路隱私權意識提高，造成電商業者的廣告，開始無法精準追蹤用戶行為，造成數位廣告成效受影響，也讓電商如何應用自己的第一方數據，越顯得重要。因此，如電商能透過自己進行會員的分群或分眾，再上傳到的數位行銷的媒體中，或為能提高行銷成效的方式之一，基於此背景，本研究目的為能透過各會員分群結果，實際應用於數位媒體上，並提出實務的行銷管理意涵。本研究透過某電商作為個案，並藉由其提供的會員交易數據，進行RFM的會員分群，然而，許多此類的會員分群研究，為利用用戶最後是否有回訪、回購或購買的貢獻度，來評估分群的成效。但在實務上，用戶的購買行為，除了自發性外，其實影響因素尚多，包含業者是否剛好促銷折扣、是否剛好有高競爭力的商品、是否有進行數位廣告的投放等，都會影響用戶是否購買，如無法排除此些因素，就用於評估集群成效，或許是不客觀的，應僅單純看用戶自發性的行為，或至少能在相同情境下。本研究透過數位行銷的FB廣告，來進行會員分群的成效實證，結果顯示在此個案中，重要價值會員的廣告投資報酬率最高，但客單價卻不一定是最高，但重要價值會員的購買頻率也會最高，因此會帶來高佔比的營收，表明此群會員是此個案的重要貢獻者。另，FB廣告能以最大轉換價值(營業額)為行銷目標，且有強大能客製化推薦商品內容給會員的能力，因此有助於提高廣告投資報酬率與客單價。最後，而在付費媒體上，對於價值會員與保持會員，發送優惠折價券會使利潤減少；對於發展會員與挽留會員，發送優惠折價券則可以再提高利潤。表示，針對價值會員與保持會員，因其多數本來就是會持續購買的會員，針對其的行銷策略，不該是發送優惠折價，造成客單價的降低，而是應設法提高購買頻率或購買金額。;With the increased awareness of online privacy rights, e-commerce advertisers have faced challenges in accurately tracking user behavior, leading to a decline in the effectiveness of digital advertising. Consequently, the application of first-party data by e-commerce businesses has become increasingly important. By segmenting and targeting their own members through digital marketing channels, e-commerce businesses can improve their marketing effectiveness. Against this backdrop, this study aims to apply member segmentation results to digital media and provide practical implications for marketing management. In this study, a specific e-commerce platform is chosen as a case study, utilizing transactional data provided by the platform to perform RFM (Recency, Frequency, Monetary) member segmentation. However, many existing studies on member segmentation evaluate the effectiveness based on the contribution of whether users revisit, repurchase, or make purchases. In practice, users′ purchasing behavior is influenced by various factors beyond their own volition, including promotional discounts, competitive products, and digital advertising campaigns. Failing to account for these factors when evaluating cluster effectiveness may lead to subjective assessments. Therefore, it is important to focus solely on users′ voluntary behavior or at least consider similar contextual situations. To empirically examine the effectiveness of member segmentation, this study utilizes Facebook advertising in digital marketing. The results reveal that in this case, the highest return on advertising investment is achieved by targeting high-value members, although their average order value may not be the highest. Moreover, high-value members also exhibit the highest purchase frequency, resulting in a substantial revenue contribution. This finding underscores the importance of this member segment in the case study. Furthermore, FB advertising allows marketers to set conversion value (revenue) as a marketing objective and offers powerful customization capabilities for recommending product content to members, thereby contributing to improved Return on Ad Spend (ROAS) and average order value.
Appears in Collections:	[Executive Master of Information Management] Electronic Thesis & Dissertation

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