Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tomer, Maansi; Siddalingappa, Rashmi; Jayapriya, J. Title A name given to the resource AI-Driven Lead Scoring: Enhancing Real Estate Decisions with Predictive Analytics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CONECCT65861.2025.11306700" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CONECCT65861.2025.11306700</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032695213?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032695213?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource and Automated Lead Scoring Systems; Feedback Loops; Lead Optimization; Lead Scoring; Machine Learning; MLOps; Natural Language Processing Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150236-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online