Optimal dynamic multi-keyword bidding policy of an advertiser in search-based advertising

• Published in: Mathematical methods of operations research (Heidelberg, Germany) Vol. 97; no. 1; pp. 25 - 56
• Main Authors: Dayanik, Savas, Sezer, Semih O.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2023; Springer Nature B.V
• Subjects: Advertisers; Advertising; Algorithms; Auctions; Bids; Budgets; Business and Management; Calculus of Variations and Optimal Control; Optimization; Competition; Dynamic programming; Mathematics; Mathematics and Statistics; Numerical analysis; Operations research; Operations Research/Decision Theory; Optimization; Original Article; Revenue; Search engines; Sponsored search advertising; 93E20; 60G55; 91B70; Stochastic modeling; Dynamic programming; Dynamic bidding
• ISSN: 1432-2994; 1432-5217
• DOI: 10.1007/s00186-022-00803-y

Sponsored search advertisement allows advertisers to target their messages to appropriate customer segments at low costs. While search engines are interested in auction mechanisms that boost their revenues, advertisers seek optimal bidding strategies to increase their net sale revenues for multiple keywords under strict daily budget constraints in an environment where keyword query arrivals, competitor bid amounts, and user purchases are random. We focus on the advertiser’s question and formulate her optimal intraday dynamic multi-keyword bidding problem as a continuous-time stochastic optimization problem. We solve the problem, characterize an optimal policy, and bring a numerical algorithm for implementation. We also illustrate our optimal bidding policy and its benefits over heuristic solutions on numerical examples.