Market crisis management strategy based on predicting loyal customers using deep meta-learning

Purpose Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is the ability to identify and retain loyal customers, who often serve as the bedrock of stability during tumultuous times. This paper aims to investigate the application of deep meta-learning analysis to predict loyal customers as a cornerstone of market crisis management. In addition, the study discusses the applicability of deep meta-learning models to Islamic banking contexts, emphasizing how Shariah-compliant customer loyalty prediction can enhance crisis management strategies for Islamic financial institutions. Special attention is given to the cultural and ethical imperatives that characterize loyalty behaviors within Islamic finance and Middle Eastern markets. Design/methodology/approach Methodologically, the paper outlines data collection, model development and evaluation procedures tailored for deep meta-learning-based customer prediction. Findings The results indicated that the overall accuracy of model 85%, a precision of 0.88, a recall of 0.82 and an F1-score of 0.85 were obtained. The analysis demonstrates the effectiveness of deep meta-learning models in accurately identifying loyal customers during market crises, offering insights into their performance and applicability compared to traditional methods. Practical implications include potential applications in crisis management for businesses and considerations for real-world implementation. Research limitations/implications The specific characteristics of the data set used may limit the generalizability of the findings. Originality/value In this work, the authors examined various aspects of using deep meta-learning models to forecast loyal customers during market crises. Recognizing the significance of customer loyalty as a stabilizing factor in such times, the authors implemented deep meta-learning as an innovative method for predicting loyal customers.