Analysis of online position auctions for search engine marketing

Margarida V. B. Santos (), Isabel Mota and Pedro Campos ()
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Margarida V. B. Santos: Universidade do Porto
Pedro Campos: Universidade do Porto and LIAAD (Lab. Artificial Intelligence and Decision Support) at INESC.TEC

Journal of Marketing Analytics, 2023, vol. 11, issue 3, No 11, 409-425

Abstract: Abstract Sponsored advertising on search engines is one of the fastest growing online advertising marketplaces. The space available for paid ads, or positions, is sold using auctions and payment is calculated considering the number of clicks each position receives. Two mechanisms are generally used in position auctions: Generalized Second Price (GSP) (e.g. Google, Yahoo!) and Vickrey–Clarke–Groves (VCG) (e.g. Facebook). To understand which mechanism guarantees the highest payoff to market players (search engines and advertisers), a multi-agent simulation is developed in Netlogo. Using the generated data, a supervised learning-based analysis on search engines and bidders’ payoffs is made using linear regression models and regression trees. Results suggest that the average payoff for auctioneers (the search engines) and bidders (the advertisers), the price for each position, and first bidder’s payment, are significantly different in the GSP and VCG mechanisms. We also found the mechanism that generates the highest payoff for the search engine is the VCG, while for the bidders it is the GSP.

Keywords: Online position auction; Generalized second price (GSP); Vickrey–Clarke–Groves (VCG); Multi-agent system; Netlogo; Regression model; Regression trees (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41270-022-00170-x

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