Response modeling for direct mailing campaigns: Revenue generation

Tripathi, Manish (2025) Response modeling for direct mailing campaigns: Revenue generation. International Journal of Science and Research Archive, 16 (1). pp. 230-240. ISSN 2582-8185

In today’s increasingly data-driven marketing landscape, direct mailing campaigns remain a powerful tool for customer acquisition and revenue generation. Central to their success is response modeling — the analytical process of predicting customer behavior to enhance targeting efficiency. This review explores the evolution of response modeling techniques, from traditional statistical models such as logistic regression to advanced artificial intelligence (AI) methods including ensemble learning, neural networks, and explainable AI. Comparative analyses demonstrate that modern machine learning models significantly improve campaign ROI and predictive accuracy. However, key challenges persist, including data imbalance, interpretability, and integration into real-time marketing systems. This review proposes a theoretical hybrid model that combines profit-based targeting with transparent AI, enabling businesses to achieve both performance and accountability. The paper concludes with a discussion on future directions aimed at enhancing scalability, fairness, and sustainability in response modeling systems.