Authors: Atharv Nitin Gore, Rushikesh Vijay Kolhe, Samarth Suresh Gaikwad, Professor Snehal Phate
Abstract: This paper proposes an AI-powered lead management platform designed to optimize sales pipeline efficiency through a Hybrid Machine Learning Classifier. The system incorporates multi-channel lead capture, XGBoost-based lead scoring, BERT-driven intent detection, lead deduplication, and CRM synchronization to enable real-time qualification and conversion of leads. With an F1 Score of 87% and AUC-ROC of 93%, it ensures a reliable and data-driven pipeline management experience. The system supports various lead sources including web forms, social media, email, and API integrations, making it highly adaptable for B2B and B2C enterprises. Ethical considerations are addressed through strong privacy safeguards, JWT-based authentication, and GDPR-compliant data management. Additionally, it minimizes manual sales effort, reduces lead response time by 89%, and enhances conversion rates by 79%. This solution establishes a reliable framework for secure, automated, and scalable lead management in digital marketing and sales operations. By leveraging advanced AI techniques such as XGBoost scoring, BERT intent detection, and fuzzy deduplication, the system effectively prioritizes high-value prospects to maximize pipeline conversion.
