Using Data Science To Increase Revenues For A Hotel By Improving Booking Cancellation Prediction By 93%

Client Overview

A hotel faced significant revenue loss due to high booking cancellations, which disrupted its pricing, inventory, and allocation strategies. To address this, the hotel sought to leverage data science to predict cancellations accurately, enabling better operational and revenue management.

Objectives

  • The client, a hotel, experienced significant revenue loss due to high rates of booking cancellations. 
  • The booking cancellations have additionally impacted their room pricing and allocation strategies.
  • The goal was to leverage improved predictions of demand to boost revenue, refine cancellation policies, and optimise overbooking and inventory management approaches.

Challenges

The hotel struggled with unpredictable booking cancellations, leading to revenue loss and inefficiencies in room allocation. Without precise forecasts, overbooking strategies and cancellation policies were ineffective, requiring a more advanced solution to optimise operations and minimise cancellations.

Solution

  • A predictive model based on artificial intelligence (AI) was developed to forecast booking cancellations. 
  • This model underwent training and testing with comprehensive data attributes provided by the hotel’s reservation system.
  • Through its dataset, a comprehensive collection of inputs was obtained, encompassing guest demographics, past booking data, types of rooms booked, duration of stay, and the channels through which bookings were made. 
  • This ensured a thorough analysis of the predictive model.

Benefits / ROI

  • The AI model achieved a 93% accuracy rate in predicting which bookings would be cancelled. 
  • This high level of precision enabled the hotel to manage potential cancellations more effectively, reducing the impact on revenue.

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