Modeling User Click Behavior in Sponsored Search

There has been significant recent interest in studying consumer behavior in sponsored search environments. Sponsored search is the fastest growing form of advertising on the Internet. A number of factors have contributed to this growth. The ads tend to be highly targeted and offer a higher return on investment for advertisers compared to other marketing methods. In addition the large audience it offers has led to a wide-spread adoption of search engine advertising. When a user issues a query on the search engine, sponsored results are displayed alongside organic search results. The organic search results are links relevant to the query and are ranked in order of their relevance. The sponsored results are ads submitted by advertisers. The advertisers submit bids for keywords that are relevant to them, along with these ads. When a user enters a query, the search engine identifies the advertisers bidding on keywords closely related to the query and uses data on bids and ad quality/performance to rank order the ads that appear in the list of sponsored results. The most widely used pricing model is the pay per click model, in which the advertiser pays only when a user clicks on his ad. The advertiser’s cost per click or cpc is determined using a generalized second price auction, i.e. whenever a user clicks on an ad in position, the advertiser pays an amount equal to the minimum bid needed to secure that position. One of the attractive features of sponsored search is that it is a highly measurable form of advertising. Data on consumer click and purchase patterns have been used to study consumer behavior and advertiser strategies. Several researchers have built random utility models to study the effect of ad position, keyword length, presence or absence of brand name, etc. on the clickthrough rate (ctr) of the ad. Rutz and Bucklin [6] propose a hierarchical bayesian model to study the conversion performance of individual keywords. They show that the model adequately addresses the sparse data problem while accounting for keyword heterogeneity. Ghose and Yang [4] use hierarchical bayesian models to understand the relationship between different metrics such as ctr, conversion rates, bid prices and keyword ranks using the advertiser’s aggregate data. They also show that the advertisers are not bidding optimally to maximize their profits. Recent work by Agarwal et. al [1] shows that although the ctr decreases with position, the conversion rate often increases and then decreases. They show that the topmost position is not necessarily the revenue maximizing position. These models have been estimated on aggregated data that catalogue advertiser’s bid, average position and total impressions, clicks and cost on a daily basis for keywords in the advertisers sponsored search campaign. The position of an ad varies across impressions during a day, but since these use aggregate data, their models assume the mean position during the day is the actual position. This aggregation of data can lead to potential biases in the estimation of parameters and ultimately affect the conclusions from these studies. The problem of aggregation bias has been addressed earlier in some detail but there is no definite answer. Kelejian [5] discusses why, under certain conditions, aggregation bias might occur and proposed a test for the existence of this bias. Allenby and Rossi [2] present an