Regression Trees for Ad Campaigns - Optimizing Online Ad Spendings

Abstract

In today’s fast-paced digital environment, it is essential for marketing professionals to allocate advertising budgets efficiently and maximize return on investment. By leveraging machine learning, particularly regression trees, key success metrics such as cost per click in online advertising campaigns can be predicted and optimized, enabling more effective budgeting decisions in marketing performance optimization. Showcasing a practical use case with a synthetic data set, this study outlines the relevant phases in applying ML to campaign optimization and demonstrates that the advantages of regression trees lie in their transparency and interpretability. Compared to more complex AI models, the results of regression trees are easier for managers to understand and translate into concrete actions. For instance, the models help managers to understand ad effectiveness of specific ad design and campaign decisions before they run costly live tests on ad platforms. Furthermore, the models allow for lean analysis of ad effectiveness across platforms. Finally, they allow capturing nonlinear effects of ad design and campaign decisions on ad effectiveness, thus avoiding overly simplistic analysis results. Therefore, this study shows the application of regression trees, as step-by-step guide for managers and actionable implications, especially for small- and medium sized enterprises adopting AI in marketing.

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