Dynamic Pricing Model for a Seasonal Beachfront Hotel

Overview

Built a dynamic pricing model for a seasonal beachfront hotel that had been setting room rates manually based on the owner's experience and a rough sense of the local event calendar. The model adjusts recommended rates daily based on booking pace, remaining inventory, day of week, and nearby events, presenting suggestions the revenue manager can accept or override rather than changing prices automatically without oversight. This addressed real revenue left on the table during high-demand weekends that were being priced the same as an average weekday, as well as slow periods that stayed priced too high to fill remaining rooms. We validated the model's suggestions against a full prior season of the hotel's actual booking data before it went live. A seasonal beachfront hotel set room rates manually based on the owner's experience and a rough sense of the local event calendar, leaving revenue on the table during high-demand weekends priced the same as an average weekday. We built a dynamic pricing model adjusting recommended rates daily based on booking pace, remaining inventory, day of week, and nearby events, presenting suggestions the revenue manager can accept or override rather than changing prices automatically without oversight. We validated the model's suggestions against a full prior season of the hotel's actual booking data before it went live, comparing what it would have recommended against what was actually charged. The revenue manager reviewed and could override every suggestion during a full season before the model's accuracy was fully trusted. Average revenue per available room rose an estimated 12% over the following peak season compared to the hotel's prior manual pricing approach, capturing demand the old approach was leaving on the table.