Predicting Campaign ROI Using Decision Trees and Random Forests in Digital Marketing - Hayadi, B Herawan; El Emary, Ibrahiem M. M. (Bright Publisher, 2024)
Digital marketing has become a cornerstone of modern business strategies, leveraging various channels and technologies to promote products and services. Measuring the Return on Investment (ROI) is crucial in evaluating the effectiveness of these marketing campaigns. This study aims to predict the ROI of digital marketing campaigns using two prominent machine learning algorithms: Decision Trees and Random Forests. The primary objective of this research is to compare the performance of Decision Trees and Random Forests in predicting the ROI of digital marketing campaigns. The study focuses on evaluating the accuracy, precision, and robustness of these models, and identifying the key features that influence ROI. The dataset used in this study comprises 200,000 rows and 16 columns, detailing various aspects of digital marketing campaigns, including campaign type, target audience, duration, and channels used. Initial Exploratory Data Analysis (EDA) identified no missing values or duplicates, ensuring a clean dataset for modeling. Data preprocessing involved feature engineering and encoding categorical variables. The models were trained and evaluated using an 80-20 split for training and testing, with cross-validation applied to ensure robustness. The Decision Tree model achieved a Mean Squared Error (MSE) of 1.0896, a Root Mean Squared Error (RMSE) of 1.0439, a Mean Absolute Error (MAE) of 0.8958, and an R2 value of -0.0781. In contrast, the Random Forest model showed superior performance with an MSE of 1.0143, an RMSE of 1.0071, an MAE of 0.8755, and an R2 value of -0.0035. Cross-validation for the Random Forest model yielded a CV MSE of 1.0035, a CV RMSE of 1.0018, and a CV R2 of -0.0039, reinforcing its robustness and accuracy. The Random Forest model's superior performance is attributed to its ability to handle complex interactions between features and its robustness against overfitting. Key predictors such as Conversion_Rate, Acquisition_Cost, and Engagement_Score were identified as significant factors influencing ROI. The study discusses the practical implications of these findings for optimizing digital marketing strategies, acknowledging the limitations of data quality and model assumptions, and suggesting directions for future research, including the integration of additional data sources and exploration of advanced machine learning techniques. This study highlights the potential of machine learning models, particularly Random Forests, in predicting the ROI of digital marketing campaigns. The findings provide valuable insights for marketers to enhance their strategies and optimize budget allocations, emphasizing the importance of predictive analytics in achieving marketing success. Future work should focus on improving model accuracy and exploring new techniques to further advance the field of marketing analytics.