A common problem for the owners of small hotels and big chains alike is how to predict customer demand so they can sell their rooms at the optimum price – until now. Assistant Professor Aysajan Eziz, along with his research partners from Washington State University, created a data-driven adaptive pricing algorithm to help hotel owners use past customer information to make better decisions about pricing.
A small hotel in the Seattle area used the algorithm for four months and saw an eight-per-cent increase in revenue. Eziz next plans to test the algorithm in several hotels in the U.S., to see if they can also improve their revenue. If so, the findings have the potential to transform the hotel industry’s approach to pricing. Eziz said many hotels pay revenue management solution providers for special software or consulting services to help them determine their pricing.
“There are some important lessons from this for business practitioners. From a hotel perspective, hotel owners don’t need to spend big money on resources, technology, or to hire revenue management staff in order to improve their revenue,” he said. “Even those revenue management solution providers don’t need to invest in such technology. They can just provide some simple data-driven solutions for hotels and still get a lot of revenue from this service.”
His future research will explore how dynamic pricing effects hotels in the long term, particularly their relationships with customers since customers are paying different prices for the same or similar services.
Listen to an interview with Aysajan Eziz