Predictive analytics for appointment bookings

TL;DR

This paper applies predictive analytics to improve appointment booking and customer show-up predictions in the financial service sector, achieving over 75% accuracy and aiding resource planning.

Contribution

It introduces two predictive models for customer appointment attendance and premium service booking, enhancing decision-making over traditional intuition.

Findings

Models achieve over 75% accuracy

Framework for demand-based resource planning

Improved decision-making in financial services

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.