Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?

Abstract

Judgmental adjustments of algorithmic predictions with the aim of improving demand forecast accuracy are a common revenue management practice. While empirical evidence on the impact of these user overrides is growing, little research attention has been given to the time horizon and the frequency in which these adjustments take place. Utilizing a multilevel regression model for repeated measures, 20,081,973 forecasts comprising seven different time horizons were analyzed. Data were collected from 1752 hotels of different hotel types belonging to 232 hotel chains in seven geographical regions. We find that the accuracy of algorithmic computer forecasts improves as time nears the date of stay and that the number of user overrides impacts this accuracy. The effect of the override frequency depends on the type of the forecasted demand and on the presence of special events. A higher number of user overrides is beneficial for group segment, but damaging for the transient segment. During special events periods, override frequency enhances accuracy.