Enhancing Sales Efficiency with AI: Implementing Random Forest and Logistic Regression Algorithms for Lead Scoring and Qualification

Abstract

This research paper explores the application of artificial intelligence, specifically Random Forest and Logistic Regression algorithms, to enhance sales efficiency through improved lead scoring and qualification. In an era where data-driven decision-making is crucial, traditional sales processes often lack the precision necessary to maximize conversion rates, leading to inefficiencies and resource wastage. By integrating machine learning techniques, businesses can better prioritize leads, optimize sales strategies, and ultimately increase revenue. The study begins by analyzing the limitations of conventional lead scoring practices and reviews existing literature on AI applications in sales. It then details the methodology employed, including the selection of relevant features from historical sales data, preprocessing steps, and the implementation of the Random Forest and Logistic Regression models. The research employs a comparative analysis to evaluate model performance using metrics such as accuracy, precision, recall, and F1 score. Results indicate that both algorithms significantly outperform traditional approaches, with Random Forest demonstrating superior accuracy in identifying high-potential leads. Logistic Regression, while slightly less accurate, offers valuable interpretability, contributing to actionable insights for sales teams. The paper concludes by discussing the implications of AI-driven lead scoring in enhancing sales productivity and proposes directions for future research, including the integration of real-time data and the exploration of hybrid models.