In modern call centers, agent performance is a key determinant of customer satisfaction, conversion rates, and operational efficiency. Traditional training and quality assurance methods often fall short of delivering personalized, scalable development paths for agents. This paper introduces a machine learning–powered Agent Skilling Algorithm that dynamically assesses, scores, and prescribes skill-building interventions based on real-time behavioural and performance data. By analysing call transcripts, sentiment, talk ratios, response timing, script adherence, and resolution effectiveness, the algorithm identifies each agent’s unique strengths and gaps. These signals are clustered into skill domains — such as objection handling, empathy, product articulation, and compliance — and matched to performance outcomes like close rates or CSAT. The system then delivers personalized, data-backed recommendations for targeted coaching, upskilling, or campaign reassignment. Designed to evolve with agent behaviour and customer expectations, the algorithm empowers leaders to shift from generic training to precision development, reducing ramp-up time, boosting morale, and driving continuous performance improvement across the floor. This framework positions the call center not just as a cost center, but as a talent engine powered by data.

Agent Skilling Algorithm, Call Center Performance, Machine Learning, Skill Development, Data-Driven Optimization, Real-Time Behavioural Data