Digital Marketing Strategies Using Artificial Intelligence: Design of a Prospect Classification Model

Abstract

This work presents the design and development of a hybrid intelligent system for prospect classification in digital marketing. The proposed approach combines unstructured textual data extracted from email content with structured behavioral data such as email opening, link clicking, and interaction frequency. The system integrates a real-time tracking module that collects and transforms user in teractions into meaningful features. These features are processed using a machine learning model based on neural networks, enhanced with sentiment analysis techniques to improve prediction accuracy. A lead scoring mechanism is implemented to classify prospects into different levels of interest, enabling more efficient targeting and decision-making. In addition, a self-learning component is introduced to continuously improve the model by incorporating newly collected data. Experimental results demonstrate strong predictive performance and stable training be havior, highlighting the system’s effectiveness in real-world marketing scenarios.