Beyond accuracy: The advantages of the k-nearest neighbor algorithm for hotel revenue management forecasting

Abstract

Revenue management (RM) systems forecast demand and optimize prices to maximize a hotel’s revenue. The RM function operates in coordination between a system and an analyst. Systems provide recommendations while analysts review the forecasts and prices to approve or make subjective adjustments. In many cases the recommendations are a “black box” with little insight regarding how recommendations are derived. This article proposes the k-Nearest Neighbor (k-NN) algorithm as a forecasting approach that can transition the “black box” to a “glass box.” The benefits of the k-NN are discussed in detail and compared with neural networks. The analysis is conducted on 35 hotels in partnership with a leading RM service provider. The results indicate similar performance for both techniques, leading to an important discussion on model evaluation outside of accuracy. In particular, the article discusses some of the unique advantages k-NN provides for the RM discipline.