document: Analyzing search engine advertising : firm behavior and cross-selling in electronic markets The phenomenon of sponsored search advertising is gaining ground as the largest source of revenues for search engines . Firms across different industries have are beginning to adopt this as the primary form of online advertising . This process works on an auction mechanism in which advertisers bid for different keywords , and final rank for a given keyword is allocated by the search engine . But how different are firm 's actual bids from their optimal bids ? Moreover , what are other ways in which firms can potentially benefit from sponsored search advertising ? Based on the model and estimates from prior work ( 10 ) , we conduct a number of policy simulations in order to investigate to what extent an advertiser can benefit from bidding optimally for its keywords . Further , we build a Hierarchical Bayesian modeling framework to explore the potential for cross-selling or spillovers effects from a given keyword advertisement across multiple product categories , and estimate the model using Markov Chain Monte Carlo ( MCMC ) methods . Our analysis suggests that advertisers are not bidding optimally with respect to maximizing profits . We conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement . We find that there exists significant potential for cross-selling through search keyword advertisements in that consumers often end up buying products from other categories in addition to the product they were searching for . Latency ( the time it takes for consumer to place a purchase order after clicking on the advertisement ) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet .
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