USE THE RIGHT DATA
TECHNOLOGY
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 .
FINDING VALUE 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 .
WHAT ' S NECESSARY ? 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 .”
“ ONE GUEST WITH ONE STAY CAN LEAVE OVER 100 DIFFERENT DATA POINTS . IT CAN BECOME A SEA OF UNMANAGEABLE DATA . IF YOU CAN ’ T MANAGE IT , YOU CAN ’ T MODEL IT PERFECTLY .”
MATT BUSCH , PARTNER , REVENUE ANALYTICS
USE THE RIGHT DATA
Not all data is created equal , says Matt Busch of Revenue Analytics . That ’ s why companies should focus on only what ’ s absolutely essential when plotting revenue strategies . Know your own customer : Busch advises hotels to focus on data from their customers first . “ You can ’ t access the data of customers who didn ’ t choose you ,” he says . “ And your competitors aren ’ t going to give it to you . The more you understand your own customer , the more intelligent decisions you can make . Frankly , I don ’ t think hotels know that much about their existing customers .” Know your competitors : Try to infer information about the competition , Busch says – usually their starting room price and their availability . Several software solutions do this , but some have gone past rate shopping and can determine demand among different room types , rather than just the starting room category . “ That ’ s something that the cruise line industry does really well ,” he says . Disregard the noise : Social media and online review sites can be helpful , Busch says , but they can also be distracting . “ There is so much noise out there that people feel like they need the perfect answer instead of the right answer ,” he says . Get your team aligned : One pitfall to applying all this data and analytics to make better decisions is that it ’ s akin to working in a vacuum . “ It ’ s more than just the technology ,” Busch says . “ It ’ s about the people , and the process and the culture .” As he points out , “ You could have the perfect model that gives you the perfect answer but it doesn ’ t matter if no one believes it , or if the team isn ’ t talking the same language and they aren ’ t ready to execute it .”
56 hotelsmag . com October 2017