Exclusive Feature: As we move deeper into 2018, hoteliers face evolving challenges and opportunities in the APAC region and there are a couple questions all hotel revenue managers should ask themselves: are you using booked or stayed data? Or, being more direct, is your revenue management system using the wrong data?

Simply put, booked data represents transactional data that indicates which room type a guest booked, and stayed data represents transactional data that specifies in which room type a guest actually stayed. Most reservations data integrated into revenue management systems is based on stayed data.

There are many reasons why the industry has always focused on stayed data. A hotel's own KPIs are based on stayed data, and it’s important to know the physical room the guests stayed in. In addition, collecting both types of data means more data to process.

Today, with advancements in machine learning, there exists a significant revenue management opportunity in shifting from stayed to booked data. Room prices can now be analytically determined through machine learning, rather than set by static room supplements. Research undertaken by IDeaS found up to 60 percent of the time, a guest’s room type changes from a lower room class to a higher one with no additional revenue attached to the change.

A page from the airline’s playbook

Airlines have long had to work around the issue of passengers booking one seat and ending up in another. In the past, airline revenue managers struggled with data sets created by scenarios where business-class upgrades occur due to loyalty program perks. For instance, let’s assume a passenger books an economy-class ticket and receives a free upgrade to business class. The flight data actuals show the business-class seat as occupied with a rate that’s likely far less than the average price of a purchased business-class ticket.

Under this scenario, the airline revenue manager might think twice about pricing the next flight’s business cabin based on the occupancy and rate of this flight if there were several complimentary upgrades to business class. To address this, airlines have learned to attribute demand and price sensitivity back to the originally booked cabin and fare so it won’t misrepresent demand for each class of service, or in the case of business class, dilute the rate for paying customers of business class.

However, airlines still upgrade because it provides a heightened customer experience and gives them the opportunity to resell economy-class tickets, where the demand is greatest, at a premium as booking windows shrink and willingness to pay increases. It also gives them a clear picture of their upgrade strategy as data will show how many upgrades to issue to fully optimise the flight and how many remaining business-class seats to hold for any demand at the business-class fare price.

The new normal for hotels

As hotel guests evolve the way in which they book and the level of service they expect increases, new technologies are needed to meet demand. Advances with today's revenue management systems are creating new efficiencies—upgrades can be strategically planned whether paid or complimentary—to serve the operational needs of the hotel and the personal preferences of the guest.

Using the right technology that leverages booked data versus stayed data allows hotels to attribute demand back to the originally-booked room type and gain, not only a more reliable demand forecast, but also a more refined revenue strategy to optimise their business. When hotels move away from manual, rules-based revenue strategy tools and reach for adaptive, machine-learning technology, there is no longer the need for room type supplements because automated revenue solution prices each room type based on the most insightful sets of data.

To capitalise on profit opportunities in today's market, hotel revenue managers need to ask themselves not only if they are using booked and not stayed data, but are their room type prices set by static room type supplements, or are they analytically determined with automated machine learning? Because if not, they could be missing out on significant revenue opportunities each and every day.

Written by: Tracy Dong, Lead Advisor, APAC, IDeaS Revenue Solutions

This is strictly an exclusive feature, reprints of this article in any shape or form without prior written approval from 4Hoteliers.com is not permitted.