This case study pertains to three of the top 5 largest hospitality companies listed on the US stock market, in terms of market cap. The chains boast of providing diverse experiences that can be enjoyed by many types of clients, including business and leisure travellers.
ForwardKeys aimed to investigate the correlation between the reported revenues of these three hospitality companies, and ForwardKeys’ Actual Air Reservations (AAR) dataset – airline reservation data. The AAR dataset is an airline bookings dataset, including most indirect bookings worldwide.
As a first step, ForwardKeys examined the historical yearly variations in the number of arriving passengers and compared it with the year-over-year variations in hotel revenues, between 2018 and 2022. The findings indicated that the yearly variations in the AAR dataset correlate very strongly with the variations in hotel revenues. The total aggregated values that were obtained for the three hotel groups were all between 0.84 and 0.9.
In the next step, ForwardKeys analysed the year-over-year variations by quarter between 2017 and 2022, which yielded even better results, with correlation figures ranging between 0.86 and 1. ForwardKeys also performed a time series correlation between the two sets of data, which yielded results between 0.74 and 0.88.
ForwardKeys then created a predictive model for these three hospitality companies’ revenues, based on future arriving passengers with data contained in the AAR dataset. And the results were encouraging, confirming the relevancy of our airline reservation data.
For the results and more detailed information about the correlation analyses and the predictive model, read the full analysis now.
