Predictive analytics for appointment bookings
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Making the case for audience design in conversational AI: Rapport expectations and language ideologies in a task-oriented chatbot
Chatbots are more and more prevalent in commercial and science contexts. They help customers complain about a product or service or support them to find the best travel deals. Other bots provide mental health support or …
ChatbotManagementAppointments: A More Effective Commitment Device for Health Behaviors
Health behaviors are plagued by self-control problems, and commitment devices are frequently proposed as a solution. We show that a simple alternative works even better: appointments. We randomly offer HIV testing appoin…
The Contextual Appointment Scheduling Problem
This study is concerned with the determination of optimal appointment times for a sequence of jobs with uncertain duration. We investigate the data-driven Appointment Scheduling Problem (ASP) when one has $n$ observation…
SchedulingFairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show
Patient no-shows is a major burden for health centers leading to loss of revenue, increased waiting time and deteriorated health outcome. Developing machine learning (ML) models for the prediction of no -shows could help…
FairnessPredictionRepresentation LearningInterdependencies of female board member appointments
We investigate the networks of Japanese corporate boards and its influence on the appointments of female board members. We find that corporate boards with women show homophily with respect to gender. The corresponding fi…