Hospitality Revenue Analytics

How to Destroy Hotel Profits with Averages #1: Outliers

I meet hospitality people every day that tell me that the widespread use of analytics will never take off in the hotel industry or that it will take decades to materialize.  We are here to fight the good fight of democratizing the use of data, math, analytics, and critical thinking across the industry. To turn hotel number crunchers into, as one of our followers put it, “ninjas”.  In that endeavor, we begin our new blog series “How to Destroy Hotel Profits with Averages.”

In the first of this new series “How to Destroy Hotel Profits with Averages”, we will look at the destructive use of the Average Booking Window.  I have met few Revenue Managers that do not make the following catastrophic mistake when calculating the Average Booking Window.  In fact, I have seen most large OTA “Intelligence” reports with this same mistake(they should really know better).  It is a also a common mistake built into most PMS and RMS calculations of the Average Booking Window.

Say you run a 50 room hotel and you’d like to figure our the average booking window for a particular Wednesday in the year because your analysis has revealed that this day is historically slow.  Marketing has agreed to launch promos for that day, but they need to know what is the average booking window so that the promo can get the most audience.

You take the arrival date minus the reservation date for all the bookings for that day and you get the following set of Days-Out numbers.


The average for this set of numbers is 19.18 so you tell the Marketing department to launch a campaign about 20 day from the day of arrival.

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Here’s the problem with this commonly used calculation. The booking at 185 days out was a couple who wanted to return to the hotel where they got engaged which is why they reserved so far out.  The 68 and 90 day bookings where independent business travelers in town for a one-time conference.  The rest of the bookings where from typical guests.

When you remove these “outliers”, the average becomes 10 days. That makes sense given that 10 Days out is the number that appears the most(the mode). By telling Marketing to launch a promo 20 days out you completely blew the prime booking window and threw your marketing dollars into the trash.  In fact, given the distribution of this data set, it is possible that no one saw the promo at all.

Averages are simple, but they are very dangerous to profit.  Yet the hotel business uses metrics based on averages as the currency that runs the industry.  Whether it be ADR, capture rates, or Average Booking Window, metrics based on averages are inexcusable in the era of advanced analytics. Keep tuned to more  “How to Destroy Hotel Profits with Averages”.

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Robert Hernandez, Statistical Analysis and Data Mining for Revenue Growth Robert is an expert in the field of mathematical Hotel Optimization and Analytics. He has spent the last 17 years building data-driven forecasting and optimization models for companies in over 20 different industries, from tech to tourism. Robert possesses a very unique skill set including cross-disciplinary experience, advanced mathematical and analytics skills, data transformation, industry-specific knowledge and business-process improvement expertise. Robert began his career at the Walt Disney Company in Revenue Planning. Read More+


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