چگونگی اندازه گیری اثربخشی تبلیغات آنلاین در بازارهای آنلاین

The online marketplace, in the form of an “open market” where a very large number of buyers and sellers participate, has occupied a rapidly increasing position in e-commerce, resulting in sellers’ increasing investment in online advertising. Hence, there is a growing need to identify the effectiveness of online advertising in online marketplaces such as eBay.com. However, it is problematic to directly apply the existing online advertising effect models to the click-through data of online marketplaces. Therefore, a model must be developed to estimate the effectiveness of online advertising in the online marketplace in terms of its characteristics. In this paper, we develop an analytical Bayesian approach to modeling click-through data by employing the Poisson-gamma distribution. Our results have implications for online advertising effect measurement, and may help guide advertisers in decision-making.

Online advertising (ad) is a form of promotion that uses the Internet and World Wide Web for the express purpose of delivering marketing messages to attract customers. The online advertising industry is expected to be stable and manifest a continuing upward trend until 2011. The compound annual growth rate is anticipated to increase by 17.4% during this period (2007–2011) and touch the $197.11 billion mark. In the coming years, online advertising spending is expected to overtake the TV advertising market. The rapid growth of this industry is being driven by the increasing number of Internet users, rising awareness, and growing broadband subscription rate and e-commerce, which is playing a key role in this industry. Not surprisingly, predicting the effectiveness of online advertising has gained much research attention. Since the Internet opened up to the general public in the mid-1990s, a database consisting of repeated customer visits to a Web site along with individual advertising exposures can be obtained by cookies to track users. Many studies (e.g., Chatterjee et al., 2003, Chan Yun et al., 2004 and Manchanda et al., 2006) have researched online advertising effect models by making use of the data. It became a new fashion to study at the individual level the analysis of advertising effects. A traditional sales-advertising model and banner advertising model has been developed and applied to estimate the online advertising effect. Meanwhile, the growing prevalence of Internet access has enabled new markets to emerge online. An e-marketplace is an electronic exchange where firms or individuals register as sellers or buyers to communicate and conduct business over the Internet. There are many types of e-marketplaces based on a range of business models such as business-to-business (B2B), business-to-consumer (B2C), or consumer-to-consumer (C2C). Perhaps the best-known marketplace except for B2B is eBay.com, an enormous globally available Auction house for products. We call a marketplace such as eBay an “online marketplace.” Thereby, the online marketplace has occupied a rapid increasing position in e-commerce. In Korea, where the B2C e-commerce market amounts to $12 billion, online marketplaces, including Auction (http://www.auction.co.kr, an eBay company in Korea), formed approximately 50% of the market in 2008.

This study introduced the Poisson-gamma model to the online advertising effect model and developed an improved model with a time-decaying effect that is applicable for the click-through data in the online marketplace. The proposed model has been experimentally proved to have advantages over the existing advertising effect models. It captured the effectiveness of each advertisement and showed significantly diminished variances compared to the previous models. This study made important theoretical contributions as follows. First, it is the first study that has adopted the Poisson-gamma model to investigate the effect of online advertising. A key contribution of this research is the development of a model that can effectively capture the effectiveness of online advertising in the online marketplace where many buyers and sellers exist and the purchase decision is made within a very limited number of visits compared to other e-marketplaces. Second, the proposed model can incorporate click-through data and is able to give inferences about advertising effects on customers who visit only once before they drop out. Third, this study has introduced the time-decaying effect based on previous research and developed a Poisson-gamma model employing that effect for click-through data. It enables the Poisson-gamma model to capture the consumers’ gradual forgetfulness of the advertisement. Today, most advertisers make an effort to find ways to maximize the effectiveness of their online advertisements in online marketplaces. A few important practical implications can be identified according to the experimental results.