On Designing Optimal Data Purchasing Strategies for Online Ad Auctions

In online advertising, advertisers can purchase consumer relevant data from data marketplaces with a certain expenditure, and exploit the purchased data to guide the bidding process in ad auctions. One of the pressing problem faced by advertisers is to design the optimal data purchasing strategy (how much data to purchase to be competitive in bidding process) in online ad auctions. In this paper, we model the data purchasing strategy design as a convex optimization problem, jointly considering the expenditure paid during data purchasing and the benefits obtained from ad auctions. Using the techniques from Baysian game theory and convex analysis, we derive the optimal purchasing strategies for advertisers in different market scenarios. We also theoretically prove that the resulting strategy profile is the unique one that achieves Nash Equilibrium. Our analysis shows that the proposed data purchasing strategy can handle diverse ad auctions and valuation learning models. Our numerical results empirically reveal how the equilibrium state changes with variation of the strategic environment.

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