Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Effective lead scoring and prioritization are critical for optimizing sales efforts and increasing conversion rates in customer relationship management (CRM) systems. Traditional rule-based approaches often fail to capture complex patterns in lead data, resulting in missed opportunities and inefficient resource allocation. This paper investigates the application of artificial intelligence (AI) techniques to automate and enhance lead scoring within Salesforce. We present a methodology for extracting and preprocessing Salesforce lead data, developing machine learning models for predictive scoring, and evaluating their impact on sales performance. Experimental results demonstrate that AI-driven lead scoring significantly improves prioritization accuracy and sales outcomes compared to manual methods.