Keyword optimization in sponsored search advertising: A multi-level computational framework

Yanwu Yang, Yinghui Yang, Xunhua Guo, Daniel Zeng

Research output: Contribution to conferencePaperpeer-review

Abstract

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 ad-group). With a real-world data obtained from past search advertising campaigns, we conduct computational experiments to evaluate our keyword optimization framework and identified 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.

Original languageEnglish (US)
StatePublished - 2013
Event23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, WITS 2013 - Milan, Italy
Duration: Dec 14 2013Dec 15 2013

Other

Other23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, WITS 2013
Country/TerritoryItaly
CityMilan
Period12/14/1312/15/13

Keywords

  • Advertising strategy
  • Keyword optimization
  • Search advertising
  • Strategy

ASJC Scopus subject areas

  • Information Systems

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