Real-Time Bid Optimization for Group-Buying Ads

TL;DR

This paper introduces a real-time bid optimization strategy for group-buying ads, accounting for time-dependent factors to maximize profits, and demonstrates significant improvements over existing methods through extensive experiments.

Contribution

It develops a novel online bidding algorithm for group-buying ads that dynamically adjusts bids based on real-time profit expectations, a significant advancement over static strategies.

Findings

Significant profit increases over existing strategies.

Robustness of bidding under various conditions.

Acceptable computation timings for real-time implementation.

Abstract

Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by the daily-deal providers. Unlike the traditional web ads, the advertiser's profits for group-buying ads depends 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 bids to maximize profits. Further, 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 multi-million click stream of 935 ads. The proposed real-time bidding, selection and admissibility show significant profit increases over the existing strategies. Further the experiments illustrate the robustness of the bidding and acceptable computation timings.