Toward an Integrated Framework for Automated Development and Optimization of Online Advertising Campaigns
Stamatina Thomaidou, Michalis Vazirgiannis, Kyriakos Liakopoulos

TL;DR
This paper introduces a comprehensive framework that automates the creation, monitoring, and optimization of pay-per-click advertising campaigns, leveraging machine learning and content analysis to improve performance and reduce manual effort.
Contribution
It presents a novel integrated architecture and methodology for semi- and fully-automated online ad campaign management, including keyword generation, campaign setup, and performance optimization.
Findings
Automated campaigns outperform manual campaigns in performance metrics.
The framework effectively learns from campaign data to optimize future investments.
Experimental results on Google AdWords demonstrate promising improvements.
Abstract
Creating and monitoring competitive and cost-effective pay-per-click advertisement campaigns through the web-search channel is a resource demanding task in terms of expertise and effort. Assisting or even automating the work of an advertising specialist will have an unrivaled commercial value. In this paper we propose a methodology, an architecture, and a fully functional framework for semi- and fully- automated creation, monitoring, and optimization of cost-efficient pay-per-click campaigns with budget constraints. The campaign creation module generates automatically keywords based on the content of the web page to be advertised extended with corresponding ad-texts. These keywords are used to create automatically the campaigns fully equipped with the appropriate values set. The campaigns are uploaded to the auctioneer platform and start running. The optimization module focuses on the…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Advanced Bandit Algorithms Research · Recommender Systems and Techniques
