PERSISTENT PATRONAGE PREDICTION: A FUZZY LOGIC FRAMEWORK FOR SUSTAINABLE CUSTOMER RETURN IN THE ECONOMICS OF BUSINESS

Abstract

 The project introduces a novel methodology, leveraging fuzzy logic principles, to predict and enhance persistent customer patronage in the dynamic landscape of business economics. The research focuses on developing a comprehensive framework that goes beyond traditional customer retention models by incorporating nuanced and imprecise customer behaviour data. The application of fuzzy logic allows for a more adaptive and flexible prediction model, accommodating the inherent uncertainty and complexity associated with customer decision-making. The proposed framework integrates diverse factors such as customer satisfaction, service quality, and personalized interactions to create a holistic understanding of the customer-business relationship. By capturing the inherent vagueness in customer preferences and the evolving nature of market dynamics, the model adapts in real-time to fluctuations in consumer behaviour. This adaptability enhances its predictive accuracy, providing businesses with valuable insights to proactively address customer needs and preferences. The project's significance lies in its potential to empower businesses with a forward-looking tool for sustainable customer return, thereby contributing to long-term economic viability. Through a series of empirical validations and case studies, we demonstrate the efficacy of the fuzzy logic framework in accurately forecasting customer patronage patterns. The findings offer practical implications for businesses seeking to cultivate enduring customer relationships in an ever-changing economic landscape.