Market Crisis Management Strategy Based on Predicting Loyal Customers Using Deep Meta-Learning DOI Open Access
Xiangting Shi,

Yakang Zhang,

Manning Yu

et al.

Published: July 11, 2024

Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is ability identify and retain loyal customers, who often serve as bedrock stability during tumultuous times. This paper investigates application deep meta-learning analysis predict customers a cornerstone market management. Drawing upon an extensive literature review, study explores previous research on crises, customer loyalty, evolution learning in contexts. Methodologically, outlines data collection, model development, evaluation procedures tailored meta-learning-based prediction. In results, overall accuracy 85%, precision 0.88, recall 0.82, F1-score 0.85 were obtained. The demonstrates effectiveness models accurately identifying offering insights into their performance applicability compared traditional methods. Practical implications include potential applications businesses considerations real-world implementation.

Language: Английский

Market Crisis Management Strategy Based on Predicting Loyal Customers Using Deep Meta-Learning DOI Open Access
Xiangting Shi,

Yakang Zhang,

Manning Yu

et al.

Published: July 11, 2024

Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is ability identify and retain loyal customers, who often serve as bedrock stability during tumultuous times. This paper investigates application deep meta-learning analysis predict customers a cornerstone market management. Drawing upon an extensive literature review, study explores previous research on crises, customer loyalty, evolution learning in contexts. Methodologically, outlines data collection, model development, evaluation procedures tailored meta-learning-based prediction. In results, overall accuracy 85%, precision 0.88, recall 0.82, F1-score 0.85 were obtained. The demonstrates effectiveness models accurately identifying offering insights into their performance applicability compared traditional methods. Practical implications include potential applications businesses considerations real-world implementation.

Language: Английский

Citations

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