Today’s revenue managers have an astounding amount of information at their fingertips to help determine room pricing and to maximize revenue, but combing through all this data in an efficient manner can be a full-time job.
A single data source can have up to 30 attributes. Indeed, that seems to be the common gripe from revenue managers about data — the sheer amount of it is so time-consuming to review that it can ultimately backfire.
“You can spend thousand of hours analyzing the data, but then you pass the time to make the right decision,” says Erick Viera, revenue director of the Fairmont Mayakoba in Mexico. A typical day for Viera involves reviewing reports about pricing, forecasting and demand, then making decisions based on those reports, studying the results and finally, communicating those outcomes with his executive team.
The need for data analysts is a looming concern for the hospitality industry, especially as pricing becomes increasingly set using analytical models. According to a survey of revenue managers by Cornell University’s Center for Hospitality Research, analytical models are expected to be the top-rated approach to determining pricing, followed by segment-based pricing and then CRM.
Additionally, the survey found that 37% of revenue managers believe that the field will become more automated with analytics. To that end, the report found that future revenue managers will need analytical skills more than they will a background in reservations or rooms.
In recent years, hotel giants such as AccorHotels and Marriott International have carved out specific positions for data scientists and analysts to evaluate all the information being collected from their hotels (and from their competitors), as well as to create and implement pricing models based on all that data. Last summer, Bethesda, Maryland-based Host Hotels & Resorts, which owns 96 properties with 54,000 rooms, created its own enterprise analytics division, of which revenue management is a part.
Yet smaller companies and independent hotels without such deep pockets often are stuck relying on software solutions to capture and analyze data, typically from their property management systems or their distribution channels. But even those may not do the job.
Tim Kayser, area director of revenue management at the Grand Geneva Resort & Spa in Wisconsin, says his resort has too many different areas of revenue for one solution to manage.
“You need software, but it’s difficult to find one that does it all for you,” he says.
Matt Busch, a partner at Revenue Analytics, which creates revenue management strategies for companies using cloud-based predictive models, says that only large franchisors with 50 properties or more, along with the big hotel chains, can really get the value of an in-house data analyst.
“At the hotel level, it’s really hard to justify that type of expense of a data scientist and to attract that talent,” says Busch, who previously worked as director of global pricing strategy for InterContinental Hotels Group.
As a result, Kayser works with different department heads at the resort to determine strategies for pricing and revenue. The property also is moving toward centralizing revenue management for the various departments, from food and beverage to spa and golf.
“It lets them take care of the customer and do the job they do best,” he says. “And it lets us, people who are more analytical, do the marketing, analysis and strategy.”
Centralizing revenue for the hotel is another shift that’s expected for the future of hospitality — which ultimately means more data for revenue managers to sift through.
Whether a hotel hires a data analyst to reel in the different nets of information or whether it keeps data gathering on the list of job responsibilities for a revenue manager, one way to ease the burden is to determine which data is absolutely necessary.
“One guest with one stay can leave over 100 different data points. It can become a sea of unmanageable data,” Busch says. “If you can’t manage it, you can’t model it perfectly.”
Still, the future of revenue management may not be completely run by algorithms or machines. Kayser says the data analyst, whether it is a human or a software, gives the information to develop a strategy, but the revenue manager is the one who actually executes it. Viera, of Fairmont Mayakoba, echoes that sentiment.
“I would like a system that gives me the optimal price based on internal and external data but with my daily interaction,” he says. “This is important. Because at the end of the day, it’s just a system.”