This is a viewpoint from Paul Van Meerendonk, director of advisory services at IDeaS Revenue Solutions.

Forecasting has been synonymous with hotel revenue management since its inception. From operational forecasts to demand forecasts, different hotel teams use and share these business projections to help establish ideal rates, allocate appropriate staff, and support property maintenance and operations.

Demand forecasts, in the world of revenue management, are a substantial component that help drive a healthy revenue strategy. This means that when it comes to ensuring the success and profitability of a hotel organization, demand forecasts are, to say the least, a big deal. This runs especially true in today’s highly digital and highly disruptive markets.

The hotel booking landscape has evolved dramatically over the past two decades. Today, hotel guests have myriad channels available that allow them to search, shop and book travel accommodations. In fact, thousands of travel site choices exist, ranging from OTAs, meta and brand.com sites to traditional voice and GDS channels.

Data decisions

And when it comes to accommodating the complexities of such a rapidly evolving booking environment, the combination of data points that revenue technology needs to consider can be endless. This also makes it critical that an advanced revenue management platform uses the right kinds of data in order to deliver the most profitable revenue results.

However, not all types of booking or consumer data are conducive inputs to the development of an optimal demand forecast. One type of data in particular that has become both insurmountable and impractical in this practice is the use of lost business data—specifically denials data.

While both regrets and denials represent a lost opportunity, they are classified with different reasons:

• A regret represents a missed booking because a rate exceeded the shopper’s budget
• A denial represents a guest turned away because the date or room type was not available

Technology that uses denials data from brand.com to create their demand forecast often produces more diluted forecasts. This is because its use makes assumptions on all behaviors of transient demand based on a small fraction of one segment. Alarmingly, this can encourage hoteliers to make big-picture business decisions based on a rather small sampling of demand.

Repeats and rebookings

The use of lost business data poses an additional risk by skewing the demand forecast with the behaviors of repeat online shoppers. For example, if a revenue or pricing platform counts every repeat shopper as a new unit of hotel demand, it can significantly inflate the demand forecast for those corresponding time periods.

Lost business data isn’t the only sneaky consideration that has the potential to harm a demand forecast. There’s another buying behavior that has been gaining popularity over the years: cancellations and rebooking. When a hotel makes rate reductions, it can cause guests who booked at a higher rate to cancel and rebook their reservations at the newly reduced rate. This ripple effect can negatively impact the performance of a hotel and trains the consumer to always perform a last minute check.

Unfortunately, the majority of revenue and pricing technology solutions on the market today aren’t capable of accounting for this specific guest behavior, by first considering the revenue implication a rate reduction may have on the hotel’s overall performance.

To ensure a hotel puts its most profitable revenue strategy forward, here are a few considerations that can help achieve this:

• Leverage revenue technology that forecasts demand through a statistical analysis approach

This means historical and future-looking data are incorporated into forecasting models—not a small sampling of data that is used to make assumptions about the rest of the business.

Historical data is important, but so is competitor rate shop info, reputation scores, future reservation data and shopping behavior across thousands of online sites.

• Ensure revenue technology integrates market demand indicators, and automates its analysis through algorithms and an optimization process

Look beyond the very manual process of analyzing dashboards and reports—leverage the unlimited possibilities of today’s powerful data through automated machine-learning.

• Select a technology provider that provides the most in-depth, accurate information

Providers should be able to account for varying guest behaviors, guest habits, competitor information, forecasts and more.

Hotels are operating in an environment of constant movement and excitement—and those that leverage big-picture data through technology and automation are the ones finding themselves reaping maximum financial benefits.

This is a viewpoint from Paul Van Meerendonk, director of advisory services at IDeaS Revenue Solutions. It appears as part of the tnooz sponsored content initiative..

Photo by Elizabeth Lies on Unsplash.