  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.

Computer Science and Game Theory;Computational Engineering, Finance, and Science