Hotel Revenue Managers Beware: The Data Scientists Are Here

Hotel Revenue Managers beware: the data scientists are here

Over the past few years, I have been writing on this blog about the inevitable shift that the hotel revenue management profession will take towards analytics and data science from the clerical-heavy function that it is now.

NB: This is an article from Origin World Labs

As improvements in technology remove many of the inefficiencies in rate distribution, the Revenue Manager will be left with the task of being more of a “thinker” than a typist.  Well, that shift has started!

More and more hotel companies are hiring data scientists. It’s real. These individuals are taking over the strategic projects where RM and Marketing do not have the analytics skills to deliver the goods.  Just like RM has taken the revenue data ownership away from Finance and Marketing, so will the Data Science department take the data dominion away from Revenue Management. Worse yet, it will take the highest salary roles away from RM also. Again, it’s inevitable.

The issue is not whether this shift will happen, but how you can prepare yourself to not be left out.  The problem for hotel RM’s is that the profile of the hotel data scientist does not coincide with the profile of most RM’s. I looked at 40 Hotel Data Science profiles on Linkedin (you can do the search yourself) and here are 7 common characteristics that they all have.

  • Obviously, the vast majority come from outside hospitality. This is not surprising as the hotel industry, which still sees itself as mostly a service business, has just not invested in developing data-science talent. This is true from the largest hotel companies to the smallest independent.
  • Only two went to hospitality school. I only found two hotel data scientists that followed the traditional hospitality path, but they each have over 12 years experience. At least they are both former Revenue Managers. More on them later on this list.
  • Most studied Math, Finance, Economics, or Engineering. Most of the hotel data scientists come with hard analytical skills and then are trained in the hotel business. Therefore, it seems that a huge investment in a hospitality education may not be that important for the more analytical roles in the industry.
  • The majority work on pricing and loyalty. Here is the rub. Most of these new data scientists seem to be working on domains that are at the core of today’s Revenue Management responsibilities. So when they take the lead from RM in pricing decisions, the typical RM will be left to upload rates, if that’s even necessary anymore.  Again, this is just what happened to desk traders on Wall Street when the Quants took away decision making from them.
  • They are a variety of ages and experience ranges. Don’t think for a second that these new data scientists are young people, fresh out of university, who don’t pose a threat to the more experienced RMs. Quite the opposite. Most are seasoned professionals who have worked in Consulting, Retail, Real Estate, and Technology for years.
  • They mostly work for big brands, but now some work for medium size hotel companies.  Hotel Data Science hiring is slowly moving down the company size spectrum.  Whereas the function started in the largest brands, now I see data scientists at smaller hotel companies.  Just like RM started at the biggest companies and then the function propagated itself throughout the industry in about a decade, so will data science.
  • The two from point #2 re-trained themselves in data science and analytics.  Let’s face it, the brands are not going to teach you analytics. Their goal is to make you smarter at their systems.  I never understood this short-sighted approach because if a Revenue Manager has strong analytical skills, they will be able to handle any system as the technology changes. I guess they are just more concerned about you taking your skills somewhere else then they are about how they would directly benefit from you possessing those skills.  The fact it that hotel companies are great at teaching hospitality, not so great at teaching analytics. These 2 RMs probably realized that after working for their companies for years and either invested in themselves or pressured their company to pay for the training.  Either way, they both proudly display their outside training on their profile page.

Opportunity for you

What will stall the shift from RM to data science is that there simply are not enough data scientists to go around and the hospitality industry is at the bottom of the hiring pyramid for analytics talent. Most data scientists go into technology, consulting, research and education, etc. In other words, non-service industries.  That leaves a talent gap that you can fill if you prepare yourself. With experience in hospitality and the right analytical skills, you will be first in line for these new opportunities.

Read more articles from Origin World Labs

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