Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework
This work addresses the problem of improving keyword decision-making for advertisers in sponsored search advertising, representing an incremental advancement with practical applications.
The paper tackles keyword optimization in sponsored search advertising by proposing a multi-level computational framework (MKOF) for decisions like targeting and grouping, and shows through experiments on real-world datasets that it outperforms common baseline strategies by approaching optimal solutions steadily.
In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users and search engines. Advertisers have to deal with a series of keyword decisions throughout the entire lifecycle of search advertising campaigns. This paper proposes a multi-level and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions. Based on this framework, we develop corresponding optimization strategies for keyword targeting, keyword assignment and keyword grouping at different levels (e.g., market, campaign and adgroup). With two real-world datasets obtained from past search advertising campaigns, we conduct computational experiments to evaluate our keyword optimization framework and instantiated strategies. Experimental results show that our method can approach the optimal solution in a steady way, and it outperforms two baseline keyword strategies commonly used in practice. The proposed MKOF framework also provides a valid experimental environment to implement and assess various keyword strategies in sponsored search advertising.