Enhancing Sales Efficiency: Leveraging Random Forest and Logistic Regression for AI-Powered Lead Scoring and Qualification

Abstract

This research paper explores the application of advanced machine learning techniques, specifically Random Forest and Logistic Regression, in enhancing sales efficiency through AI-powered lead scoring and qualification. The study addresses the challenge faced by sales teams in prioritizing leads and improving conversion rates by utilizing predictive algorithms to identify high-potential prospects. Through a comprehensive analysis of historical sales data, the paper demonstrates the superior accuracy and reliability of Random Forest and Logistic Regression models compared to traditional heuristic methods. The methodology involves training these models on a diverse dataset containing demographic, behavioral, and engagement variables, followed by rigorous validation to ensure robust performance across different business contexts. Key findings reveal that Random Forest consistently outperforms Logistic Regression in terms of classification accuracy and the ability to handle non-linear relationships, while Logistic Regression provides more interpretable insights into the feature significance influencing lead conversion. The integration of these models into the sales qualification process resulted in a significant increase in conversion rates and sales efficiency, highlighting the practical benefits of adopting machine learning strategies in sales operations. The paper concludes with recommendations for implementing AI-driven lead scoring systems, emphasizing the need for continuous model retraining and stakeholder collaboration to adapt to evolving market dynamics.