Understanding the Business of Online Affiliate Marketing: An Empirical Study
Haitao Xu, Yiwen Sun, Kaleem Ullah Qasim, Shuai Hao, Wenrui Ma, Zhenyuan Li, Fan Zhang, Meng Han, Zhao Li
Abstract
Affiliate marketing is a revenue-sharing marketing scheme by which an affiliate, such as a blogger or YouTuber, garners commissions for promoting a merchant's goods or services, thereby aiming to foster a mutually beneficial relationship between affiliates and merchants. Despite being a multi-billion-dollar global industry, affiliate marketing remains inadequately explored, and the research community lacks a comprehensive understanding of its intricate ecosystem. In this paper, we present the first comprehensive empirical study of the affiliate marketing ecosystem. We conduct thorough measurements to assess the prevalence of affiliate marketing, estimate the market size, and elucidate the characteristics of affiliates, merchants, and intermediary affiliate networks. Over a continuous span of 13 months, we monitored four of the most prominent affiliate aggregation platforms, yielding a substantial dataset. We observed 467,219 unique offers - tasks to be undertaken by affiliates - involving 37,109 merchants and 556 affiliate networks across the four platforms. Notably, these offers would cost the merchants more than 19 million USD for the completion of all the actions pre-defined in these offers, such as signing up or making a transaction. Additionally, we compiled a large-scale dataset comprising 124,462 affiliate links, enabling us to conduct a comprehensive investigation. Finally, we propose machine learning models incorporating the characteristics of affiliate links to detect real-world affiliate marketing campaigns.
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