AI-Driven Lead Scoring: Enhancing Real Estate Decisions with Predictive Analytics
and Automated Lead Scoring Systems; Feedback Loops; Lead Optimization; Lead Scoring; Machine Learning; MLOps; Natural Language Processing
Lead optimization remains underutilized in customer acquisition, with businesses often focusing on new models rather than refining existing processes. Many overlook automation, real-time data, and feedback loops that enhance insight into lead behavior. Automated optimization continuously improves lead scoring by fine-tuning models over time. Current approaches rely on basic lead scoring without real-time data integration or continuous updates. This research focuses on machine learning-driven lead optimization to improve scoring accuracy and personalized communication. We propose an AI-enhanced system that integrates CRM data with predictive models using ensemble techniques like Random Forest and XGBoost. Our approach achieves high accuracy in property hotspot and ROI prediction, with R2 values up to 0.99. However, a 5% uncertainty exists, requiring carefully generated synthetic datasets. This methodology improves lead prioritization, decision-making and data-driven strategies, ultimately increasing conversion rates and revenue growth. 2025 IEEE.
Tomer, Maansi; Siddalingappa, Rashmi; Jayapriya, J.
2025 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/CONECCT65861.2025.11306700 https://www.scopus.com/pages/publications/105032695213?origin=resultslist
Tomer M., Christ University, Department of Computer Science, Karnataka, Bangalore, 560073, India; Siddalingappa R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560073, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, 560073, India