Luchi, PieroHeo, Cindy YoonjoungNobre Pereira, LuisViverit, LucianoContessi, Daniele2025-07-182025-07-182025-100278-4319http://hdl.handle.net/10400.1/27446All hotels receive numerous booking requests every day, either directly or through online travel agencies, but only a small percentage of these requests are converted into reservations. Low conversion rates generate an additional layer of uncertainty into the hotel demand function and pose a challenge for revenue maximization. Consequently, optimizing the conversion rate is a top priority for all hotel managers. Despite its importance, the factors influencing the conversion rate are not yet well understood. This longitudinal study aimed to identify the factors that explain seasonal variations in the conversion rate, providing insights to optimize it. By segmenting stay dates using machine learning algorithms and employing a logistic regression model to predict the probability of conversion per segment, this innovative research proposes a framework for conversion rate optimization. The research note contributes a new data mining methodology that can be implemented in machine learning algorithms to enhance conversion ratesengBooking conversionE -commerce in hospitalityConversion rateHotel demandHotel bookingjournal article10.1016/j.ijhm.2025.104313