Cyclical Bid Adjustments in Search-Engine Advertising

Authors:
Xiaoquan (Michael) Zhang;Juan Feng
Affiliations:
Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Department of Information Systems, College of Business, City University of Hong Kong, Kowloon, Hong Kong
Venue:
Management Science
Year:
2011

Citing 15
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Abstract

Keyword advertising, or sponsored search, is one of the most successful advertising models on the Internet. One distinctive feature of keyword auctions is that they enable advertisers to adjust their bids and rankings dynamically, and the payoffs are realized in real time. We capture this unique feature with a dynamic model and identify an equilibrium bidding strategy. We find that under certain conditions, advertisers may engage in cyclical bid adjustments, and equilibrium bidding prices may follow a cyclical pattern: price-escalating phases interrupted by price-collapsing phases, similar to an “Edgeworth cycle” in the context of dynamic price competitions. Such cyclical bidding patterns can take place in both first-and second-price auctions. We obtain two data sets containing detailed bidding records of all advertisers for a sample of keywords in two leading search engines. Our empirical framework, based on a Markov switching regression model, suggests the existence of such cyclical bidding strategies. The cyclical bid-updating behavior we find cannot be easily explained with static models. This paper emphasizes the importance of adopting a dynamic perspective in studying equilibrium outcomes of keyword auctions. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.