Experience-Led Learning Optimization Model for Hotel Pricing

Inspired by interviews with hotel managers and analysis of real pricing data, we develop an experience-led pricing algorithm called the DDD algorithm, which derives actionable decisions from historical data. DDD stands for “Data Collation”, “Demand Learning,” and “Decision Optimization.” In data collation, data that are similar to the current scenario and valuable for learning are selected for demand learning. In demand learning, due to the scarcity of data, traditional demand learning methods are not effective. Therefore, we adopt a novel approach, the core idea of which is to infer the cause (i.e., demand parameters) from the outcome (i.e., decisions). In decision optimization, the demand parameters are used to determine the final pricing decision, with a balance between exploration and exploitation. We evaluate the DDD algorithm based on revenue regret and computational complexity. The revenue regret of the DDD algorithm is O(TK), and its time complexity is O(N3+KL5.5), where N is the size of the historical dataset, T is the decision times, K is the quantity of selected data and L represents the number of days in advance for sale. Finally, the effectiveness of the DDD algorithm is demonstrated through numerical experiments using real hotel data.