یک آزمایش تجربی برای اندازه گیری اثربخشی تبلیغات آنلاین در بازار آنلاین با استفاده از یک مدل سلسله مراتبی بیز

Online marketplace, taken the form of “open market” where a very large number of buyers and sellers participate, has occupied a rapid increasing position in e-commerce, which resulting in sellers’ increasing investment on online advertising. Hence, there is a growing need to identify the effectiveness of online advertising in the online marketplaces such as eBay.com. However, it is problematic to directly apply the existing online advertising effect models for click-through data of online marketplaces. Therefore, there is a need for developing a model to estimate the effectiveness of online advertising in online marketplace considering its characteristics. In this paper, we develop an analytical Bayesian approach to modeling click-though 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 expressed purpose of delivering marketing messages to attract customers. Online advertising industry is expected to be stable and manifest a continuing upward trend till 2011. The compound annual growth rate is anticipated to increase by 17.4% during this period (2007 to 2011) and touch the $197.11 billion mark. In coming years online advertising spending is expected to overtake the TV advertising market. The rapid growth of this industry is being driven by increasing Internet users, rising awareness and growing broadband subscription rate and e-commerce, which is playing a key role in this industry. Not surprisingly, how to predict the effectiveness of online advertising has gained lots of research attention. Owing to the Internet opened up to the general public in the mid-1990s, a database consisting of repeated customer visits to a website along with individual advertising exposures can have been obtained by cookies to track users. A lot of studies (e.g. Chan Yun et al., 2004, Chatterjee et al., 2003 and Manchanda et al., 2006) have researched about online advertising effect models by making use of the data. It became a new fashion to study on an individual level analysis of advertising effects. Traditional sales-advertising model and banner advertising model has been developed and applied to estimate the online advertising effect.

This study has introduced the Poisson–gamma model to 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 experimentally proved to have the advantageous over the existing advertising effect models. It is able to capture the effectiveness of each advertisement and shows 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 Poisson–gamma model in order to investigate the effect of online advertising. A key contribution of this research is the development of model which can effectively capture the effectiveness of online advertising in the online marketplace where a lot of buyers and sellers exist and purchase decision is within a very limited number of visits compared to other e-marketplace. Second, the proposed model can incorporate click-through data and is able to give inference about advertising effects from the 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.