| 摘要(英) |
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. |
| 參考文獻 |
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