An empirical test to forecast the sales rank of a keyword advertisement using a hierarchical Bayes model

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. Not surprisingly, how to predict the effectiveness of online advertising has gained lots of research attention. This study introduces the hierarchical Bayesian analysis to the online advertising effect model involving competition with other products. It developed a competition model with a time-decaying effect that is applicable for the sales-rank data in the online marketplace. The proposed model formalizing the hierarchical structure has performed better than the reduced model without having random effect components. It captures the heterogeneous advertising responses across the products as well as search keywords. Our results have implications for online advertising effect measurement, and may help guide advertisers in decision-making.

► Sales quantity of a product in an online marketplace strongly depends upon its previous sales rank. ► It would be more accurate to adopt the sales rank as a dependent variable instead of sales itself. ► Multi-level hierarchical Bayesian models incorporating with heterogeneous product characteristics might perform better off.