A HYBRID ML FRAMEWORK FOR PERSONALITY-BASED CUSTOMER SERVICE OPTIMIZATION

Abstract

This research introduces a hybrid machine learning framework that optimizes customer service by incorporating personality. The goal is to enhance service efficiency and consumer satisfaction by meticulously examining their behavior. The framework employs machine learning algorithms, natural language processing, and personality prediction techniques to ascertain the personality characteristics of a client by analyzing their communication patterns, preferences, and previous interactions. Clients are categorized according to their personality traits in the recommended methodology. This allows businesses to offer them more personalized responses, suggestions, and methods of support. The hybrid approach combines predictive analytics and categorization to enhance consumer behavior prediction and facilitate decision-making. The framework enhances the overall quality of service in digital customer support environments, reduces response times, and increases customer engagement, as demonstrated by an experiment. The research also illustrates the importance of AI-based personalization in modern CRM systems and provides a practical solution for improving customer service in a variety of sectors.