Real-Time Bid Optimization for Group-Buying Ads

Abstract

Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by daily-deal providers. Unlike traditional web ads, the advertiser’s profits for group-buying ads depend on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts and dynamically vary the bids to maximize profits. Furthermore, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real-time ad selection, admissibility, and real-time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection, and admission strategies on a multimillion click stream of 935 ads. The proposed real-time bidding, selection, and admissibility show significant profit increases over the existing strategies. Further experiments illustrate the robustness of the bidding and acceptable computation timings.