A Model for Lead Scoring and Prioritization using AI Techniques

Abstract. - Lead scoring is essential for customer relationship management (CRM), allowing businesses to rank potential customers based on conversion probability. Traditional methods, relying on manual evaluation or rule-based sys- tems, often lack accuracy and scalability. This study presents an AI-driven ap- proach that leverages machine learning to automate lead scoring and prioritiza- tion. The proposed model utilizes algorithms like logistic regression, random forests, and support vector machines to analyze historical customer data and predict con- version likelihood. By automating this process, businesses can enhance accuracy, optimize resource allocation, and improve decision-making. Key aspects include data preprocessing, feature selection, model training, and performance evaluation using metrics like accuracy, precision, and recall. This AI-based model streamlines lead management, reduces human bias, and im- proves sales efficiency. The findings demonstrate the potential of machine learn- ing in optimizing lead prioritization, increasing conversion rates, and minimizing acquisition costs, paving the way for future advancements in AI-driven CRM strategies. Keywords: Lead Scoring; Artificial Intelligence; Machine Learning; Predictive Analytics; Customer Relationship Management