Predictive analytics for appointment bookings

2022/04/18 by MA Nang Laik, Laik, MA Nang
Business, Management and Accounting · #Customer churn and segmentation #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2204.08475

openalex publication_date 2022/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

One of the service providers in the financial service sector, who provide premium service to the customers, wanted to harness the power of data analytics as data mining can uncover valuable insights for better decision making. Therefore, the author aimed to use predictive analytics to discover crucial factors that will affect the customers' showing up for their appointment and booking the service. The first model predicts whether a customer will show up for the meeting, while the second model indicates whether a customer will book a premium service. Both models produce accurate results with more than a 75% accuracy rate, thus providing a more robust model for implementation than gut feeling and intuition. Finally, this paper offers a framework for resource planning using the predicted demand.

Related

• Microservices-Based Framework for Predictive Analytics and Real-time Performance Enhancement in Travel Reservation Systems
• A machine learning approach to itinerary-level booking prediction in competitive airline markets
• Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis
• Learning to Match
• Hotel Booking Cancellation Prediction Using Applied Bayesian Models