Using five years of data collected from a small and independent hotel in The Netherlands this case study explores RMS data as a means to seek new insights into occupancy forecasting. The study provides an insight into the random nature of group cancellations, an important but neglected aspect in hotel revenue management modeling. The empirical study also shows that in a local market context demand differs significantly per point of time during the day, in addition to a seasonal monthly and weekly demand pattern. Moreover, the study presents evidence on the inhomogeneous Poisson nature of the probability distribution function that demand follows, a crucial characteristic for forecasting modeling that is generally assumed but not reported in the hotel forecasting literature. This implies that demand is more uncertain for smaller than for larger hotels. By reporting the results of an in-depth case study, this paper seeks to draw attention to the critical and often overlooked role of exploratory data analysis in hotel revenue management forecasting. Implications for theory and directions for future research are provided.