Evaluation of the effectiveness of implementing artificial intelligence in the Google Advertising service

Document Type : Research Paper

Authors

1 Professor, Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine.

2 Аssociate professor, Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine.

3 Associate Professor of the Department of Digital Technologies and Design- Analytical Solutions, Technical University Metinvest Polytechnic LLC – Pivdenne highway, 80, Zaporizhzhya, Zaporizhzhya region, 69008, Ukraine.

4 Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine.

5 Master of Management, postgraduate student of the third level of higher education, majoring in «Economics», State University of Intelligent Technologies and – Kuznechna street, 1, Odesa, Odesa region, 65000, Ukraine.

10.22059/jitm.2024.99052

Abstract

This paper examines the effectiveness of implementing artificial intelligence (AI) in the Google Ads advertising service. The study analyzes the advantages and disadvantages of AI integration, focusing on attribution models and end-to-end analytics. The findings show that traditional metrics, such as CTR, CPC, and ROI, used to evaluate advertising campaign performance, exhibit significant statistical errors when AI tools are applied, with errors reaching up to 35%, exceeding typical business margins. A comparative analysis in the construction industry highlights discrepancies of 10% to 35% between traditional and AI-driven models. The study concludes that universal AI algorithms often fail to account for industry-specific dynamics, leading to inaccurate evaluations. The practical significance of this research lies in proposing an alternative approach that combines traditional evaluation methods with AI-based tools, offering a more reliable framework for assessing campaign effectiveness

Keywords

References

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Journal of Information Technology Management
Volume 16, Issue 4
2024
Pages 79-99

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