Optimizing Publisher Revenue in Digital Marketing Using Decision Trees and Random Forests

This study explores the optimization of reserve prices in real-time first price auctions within digital advertising using decision tree and random forest algorithms. The dataset used includes 567,291 entries covering various variables such as impressions, bids, prices, and revenue, providing a comprehensive view of auction dynamics over a full year. The decision tree model achieved a Mean Squared Error (MSE) of 0.1347 and an R² score of 0.731, indicating a reasonable level of accuracy in predicting reserve prices. In contrast, the random forest model significantly outperformed the decision tree model with an MSE of 0.0789 and an R² score of 0.842, demonstrating superior predictive power and robustness. The analysis revealed that the application of these machine learning models significantly enhances the accuracy and reliability of reserve price predictions, helping publishers to optimize their revenue. The findings show that by setting optimal reserve prices based on the models' predictions, publishers can minimize the risk of underselling ad inventory and maximize revenue, as evidenced by a 15% increase in revenue observed in a case study after implementing the random forest model. The study also provides insights into bidder behavior, particularly bid shading strategies, highlighting how bidders adjust their bids in response to different reserve price settings. Higher reserve prices tend to reduce bid shading, resulting in more competitive and balanced auctions. The practical implications for digital marketing include enhanced strategic decision-making for publishers and a more transparent and predictable bidding environment for advertisers. Despite the promising results, the study acknowledges limitations such as reliance on historical data from a single ad exchange platform and the assumptions inherent in the models. Future research should expand the dataset to include multiple platforms and explore more advanced machine learning techniques to further improve reserve price optimization. Overall, this research underscores the potential of leveraging data science and machine learning to transform digital advertising strategies, driving higher revenue and efficiency in the industry.