AI-powered customer profiling and segmentation to reduce booking cancellations

In sectors including travel, hotels, and e-commerce, cancellations of reservations present a serious problem since they result in lost income and inefficient operations. This research introduces an AI-powered customer profiling and segmentation framework to predict and reduce booking cancellations. Utilizing Random Forest for customer profiling and K-Means clustering for segmentation, this study identifies critical factors influencing cancellations, such as age, booking frequency, satisfaction levels, refund preferences, and loyalty scores. The Random Forest model achieved an 89.7% accuracy, effectively classifying high-risk customers, while K-Means clustering revealed nine distinct customer segments, enabling personalized retention strategies. To validate segmentation, Elbow Method, Silhouette Score (0.19), and Davies-Bouldin Index (1.43) were employed, confirming well-defined clusters. Findings indicate that Price-Sensitive and Refund-Oriented Customers exhibit higher cancellation tendencies, requiring flexible refund policies and targeted incentives. Conversely, Loyal Customers demonstrate high retention potential, suggesting loyalty-based engagement programs. By integrating AI-driven insights, businesses can proactively minimize cancellations, optimize marketing efforts, and enhance customer satisfaction. This research contributes to AI-based customer retention by providing a scalable, data-driven strategy for predictive analytics and segmentation. Future work will explore real-time adaptive AI models, reinforcement learning-based interventions, and sentiment analysis to enhance predictive accuracy, ensuring smarter, more personalized customer management solutions.