Volume 6 • Issue 1 • PP: 15–28 • 2026
Optimizing Digital Marketing Revenue Forecasting Using an XGBoost–Dipper Throated Optimization Hybrid Model
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The explosive growth of digital marketing data and the increasing need for accurate revenue forecasting have driven the adoption of advanced Machine Learning (ML) techniques capable of modeling complex, nonlinear relationships in dynamic environments. Motivated by the limitations of traditional linear forecasting methods, this study proposes an optimized predictive framework that integrates the Extreme Gradient Boosting (XGBoost) algorithm with a novel metaheuristic, Dipper Throated Optimization (DTO), to enhance model performance on temporal marketing data. The key contribution of this work lies in combining ensemble learning with bio-inspired optimization to achieve superior predictive accuracy and stability in Time-Series forecasting tasks. As the experiments of the Digital Marketing Metrics dataset demonstrate, the original XGBoost model achieved a Mean Squared Error (MSE) of 0.0905 and a coefficient of determination (R2) of 0.8007, and the optimized XGBoost+DTO model has significantly improved results, with an MSE of 0.0010 and a coefficient of determination (R2) of 0.9002. These results support the argument that DTO is effective in hyperparameter optimization and reducing generalization errors. The results of this paper are not unique to digital marketing, and the authors have presented a scalable, interpretable optimization model that can be generalized to other data intensive fields, such as financial analytics, demand forecasting, and customer behavior modelling. The study is a good step in the right direction of creating more accurate, adaptive and data-driven decision-making in the digital economy by integrating ML and nature-inspired optimization.
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