Optimizing Digital Promotion Decision Making with Data-Driven Marketing

Abstract

In today’s digital marketing landscape, organizations must optimize promotional strategies using data-driven insights. This study proposes an integrated framework combining machine learning (ML) and multi-criteria decision-making (MCDM. Using 1,200 campaigns across platforms, we applied and evaluated regression models—Linear Regression, Random Forest, and Gradient Boosting—to predict ROI from key attributes. Gradient Boosting performed best (R² = 0.82), identifying engagement score, conversion rate, and click-through rate (CTR) as top factors. Used the Analytic Hierarchy Process (AHP) to prioritize campaigns based on predicted ROI, engagement, CTR, and cost-per-click (CPC). This combined approach supports marketers in balancing data-driven accuracy with managerial judgment. A/B testing showed the model-informed group achieved a 17.6% higher ROI and lower CPCs than baseline strategies. This research advances marketing analytics by merging advanced ML with structured decision-making, providing a replicable method for enhancing promotional effectiveness. The findings highlight the significance of behavioral metrics in predicting success and the value of integrating algorithmic precision with human evaluation. This blended approach empowers marketers to move beyond single-metric optimization, enabling more informed and impactful digital marketing strategies in competitive environments.