Optimizing Digital Marketing Revenue Forecasting Using an

XGBoost–Dipper Throated Optimization Hybrid Model

Mohamed Rabehi1 Abdelaziz Rabehi2,*

1 Laboratory Department of Civil Engineering, University of Djelfa, 17000 Djelfa, Algeria

2 Telecommunications and Smart Systems Laboratory, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria

Received: December 10, 2025 Revised: February 05, 2026 Accepted: April 04, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Digital Marketing Analytics Machine Learning (ML) Extreme Gradient Boosting (XGBoost) Dipper

Throated Optimization (DTO) Revenue Forecasting

1. INTRODUCTION

The fact is that the development and introduction of information

technology in the world economy have driven unprecedented

growth in information creation, especially in

digital marketing. Companies across all sectors are turning

to digital platforms to reach their customers, customize their

campaigns, and gauge their marketing effectiveness. This has

led businesses to face large volumes of non-homogeneous

data resulting from websites, social media, email campaigns,

and internet transactions. The problem, however, lies not

only in gathering such data but also in efficiently analyzing

it to produce actionable insights that improve marketing performance

and increase profitability. In this regard, accurate

revenue forecasting has emerged as a burning, though continuous,

issue. E-business organizations widely use promotional

campaigns to reach specific consumer groups, improve consumer

engagement, and boost sales [1]. Nevertheless, revenue

performance is not well-predictable due to the dynamism of

digital markets and multifactorial relationships among con-