Group Buying on the Web: A Comparison of Price-Discovery Mechanisms
Management Science
AdWords and generalized online matching
Journal of the ACM (JACM)
Expressive banner ad auctions and model-based online optimization for clearing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Bidding for Representative Allocations for Display Advertising
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Adaptive policies for selecting groupon style chunked reward ads in a stochastic knapsack framework
Proceedings of the 20th international conference on World wide web
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Bidder's strategy under group-buying auction on the Internet
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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 the 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. We evaluate the proposed bidding on a multi-million click stream of 935 ads. The method shows significant profit improvement over the existing strategies.