Estimating advertisers' values for paid search clickthroughs

Paid search is an important form of online advertisement. Clickthroughs from slots are bid for by advertisers. The process of formulating bids is a complex one involving bidders in competing against other advertisers in multiple auctions. It would be helpful in managing the bidding process if it were possible to determine the values placed on a clickthrough by different advertisers. The theory of two models for estimating advertiser values and associated parameters is presented. The models are applied to a set of data for searches on the term Personal Loans. The results of the model that fits the data better are evaluated. The utility of the model to practitioners is discussed. Some issues raised by the results about the role of bidding agents and the discriminatory power of Customer Relationship Management systems are considered. Ways to develop the preferred model are outlined. It is suggested that the model has implications for evaluating forecasting methods for use in paid search auctions.