Hotel Experts Say AI Will Make Room Pricing More Profitable
Hotel companies hope artificial intelligence (AI) will improve their skill at pricing rooms. Known as revenue management, the field relies on forecasting and is ripe for disruption.
The changes won’t happen overnight. Hotel revenue managers have worries about the security and reliability of today’s generative AI. But within a few years, software makers will likely overcome the flaws and hotels will reap efficiency gains.
“A lot of proposed applications of generative AI feel like solutions in search of a problem, but revenue management is a perfect use case for it,” said Jeff Edwards, a consultant and former IHG executive. “It’s data-intensive, and it’s too complex for humans to manage in real-time.”
Instilling Trust
A big win would be if AI gave hotel decision-makers more confidence in automated rate recommendations.
Today’s revenue management software typically produces tables or spreadsheets, leaving it up to revenue managers to interpret them.
“With all due respect to the vendors, at too many hotels today, people turn off the computer’s rate recommendations because they’re skeptical — especially as a date approaches,” Edwards said.
AI could let managers ask questions via a chat interface, clarifying the assumptions behind any particular rate suggestion in plain English. That could build trust in the recommendations.
Generative AI could push insight into revenue management up the organizational hierarchy, said Darren Koch, chief product officer at Duetto, whose pricing and related tools are used by more than 4,000 hotel and casino resort properties.
“Today you have asset managers and owners who have perceptions of what the value of their asset is … of what the market will pay,” Koch said. “Sometimes those perceptions are vastly wrong.”
“They may think ‘the team is just not executing properly — get me the €1,000 a night that I deserve,'” Koch said. “But in the future, a computer might tell them that they need to renovate their guest rooms because an analysis of guest reviews, scores, and comments show that the average review score is negatively interacting with the rate.”
