Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework

TL;DR

This paper introduces a multi-level computational framework for optimizing keywords in sponsored search advertising, improving decision-making across campaign levels and outperforming common baseline strategies.

Contribution

The paper presents a novel multi-level, closed-form framework for keyword optimization, enabling effective decision support in search advertising campaigns.

Findings

Framework approaches optimal solutions steadily

Outperforms baseline keyword strategies

Provides a valid environment for strategy assessment

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 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.