Picture this: You’re a revenue manager. You follow all the rules. And you’re very diligent at it too. When setting the asked for room price for any specific day, you check your hotel’s historical rate records, examine what your comp set is charging, and even notice that Beyoncé is in town on that night, so you pump up the rate a few bucks more. Seems like the right thing to do, right?
NB: This is an article by Ravneet Bhandari, Chief Executive Officer at LodgIQ
Perfect! Or is it?
After all, this is the way smart revenue managers and hotel operators have been setting rates for years, and its helped make hotels a lot of money. Particularly, in recent years for many global markets. How could it not be the right thing? Especially since many hotels are probably enjoying impressive revenue.
Yet, that’s not the case at all. While you were out pricing hotel rooms with what you assured yourself was a full complement of confidence, the rules went ahead and changed. And with it, everything you thought you knew about perfect pricing evaporated.
To thrive today, we have to understand those procedures that helped us for years are no longer the holy grail in the way they once were and that industry, guests and data applications have moved on.
Today, pricing with confidence means making decisions based on reams of previously undetectable information. Valuable data we never realized existed because it was invisible to us. We’ve had the illusion we’ve attained perfect pricing, but that wasn’t the case at all. The good news is, though seemingly more complex, finding confidence in pricing is not as difficult as it seems at first blush.
New Rules of Loyalty
There’s been plenty of discussion regarding the true nature of loyalty; we hear about it constantly at lodging industry events around the world. This is of course important because theoretically loyal customers are not as price conscious. They will pay a premium, but to achieve perfect pricing we need to adjust the rules here too.
While some argue overall consumer loyalty is waning, we see it differently at LodgIQ. The way customer loyalty is expressed is changing, not the notion of loyalty itself. According to a Facebook survey, 77% of people buy repeatedly from the same brand. In 2017, customers are apt to be loyal to a portfolio of brands from a single hotel company, or book the bulk of their stays within several brand families. We all know those people who have achieved a high level of status with two or more hotel companies.
Modern loyalty program customers have different stay requirements each time they travel, setting the stage for them to stay differently with each trip. Plus, during the last 15 or so years, lodging companies have introduced hundreds of brands appealing to smaller and smaller niche categories of people and preferences, eschewing a one size fits all approach.
Now brands exist that cater to the millennial mindset, plush luxury experiences, extended stays, resorts, lovers of the boutique experience, and the classic conference hotel, to name a few. The fractured nature of hotel products has splintered the customer base, pushing people to an array of exciting new products that express different parts of the guest personality. Each type of property relevant at different times.
A loyal guest could be attending a conference one week and desire being near the convention center. The next trip, he or she may use a boutique hotel to host a client meeting. Then, perhaps, the same customer is bringing the family along on another trip to attend a family wedding. In this case, he or she could be looking for a more value priced select service product.
Pricing with confidence means inherently understanding a customer no longer have static needs. The same guest will be seduced by a different price every time, though many customers are desiring the loyalty component.
It’s a Goldilocks story: The price you post may be too hot, too cold, or just right. Each time a customer travels, the temperature at which they will feast on porridge is different. Hoteliers must inherently understand that serving up the just right temperature for a specific guest will change from stay to stay.
It’s a revelation upending everything the industry has held sacrosanct regarding perfect pricing. The rules of customer engagement have permanently changed, and revenue managers must recognize and adapt to this new reality. Yes, customers still want to be loyal, but that loyalty is expressed differently – and at a different price point — with each stay.
It’s Not the Same Old BAR
We’re all familiar with BAR, ‘Best Available Rate’. In 2017, pegging rates to a benchmark BAR rate can be more effective and efficient than ever before.
Historically, setting the most effective BAR rate was part science, part guessing game. It’s hard to find any smart professional that hasn’t been comfortable with this methodology. After all, hoteliers were basing future forecasts on solid historical hotel data. And it worked. Rate setting wasn’t some random guessing, but one with substantive information; especially when incorporating a hotel’s comp set’s rates into the forecasting process.
This is what we call inside-out forecasting, the art of using traditional data points as a way to determine future looking pricing thresholds. While it’s served the hotel industry well over time, there is so much more information available now than at any other point in history, that, when used properly, that data can help us refine BAR to create more accurate forecasting.
According to IBM, 90 percent of all data ever created was within the last two years alone. If we can tap into this information treasure trove, and make sense of those endless data streams, hoteliers can set an increasingly accurate BAR rate. A new term has arisen to describe the process of utilizing all the emerging data points; Outside in forecasting.
It’s the idea of taking all that great information previously utilized, and combining it with previously undetectable data sets. But being able to easily understand all that data, means we need some new tools from which to enhance our understanding of all this complex information.
New Era, New Tools
To understand how to maximize revenue for any given night, we must first shift our perception. We’re in a post revenue management era, we’ve quietly moved into the dawning of the revenue optimization era. It’s the understanding we are not trying to find best overall rate, but a select group of rates that in aggregate create the most profitable business mix.
The essence here is cooking up several different temperatures of porridge to appeal to different tastes. It’s the understanding that people want to stay at your hotel for different reasons and therefore have a different price in mind they’re willing to pay. The fervent Beyoncé concert goer is likely willing to pay more for a room than a business traveler who can visit that city that week, or the week after when rates might be more approachable.
To accomplish perfect pricing in the new era, we need to rely on new tools. This is where those buzzwords we’ve been hearing a lot about lately come into play. Pricing with confidence can be achieved only by leveraging the power of Big Data and machine learning.
Big Data refers to the emergence of those previously mentioned invisible data sets. Obviously, a hotelier can make more educated pricing decisions if he or she has more information from which to base a decision. Big Data allows smarter decisions to be made. But only if the signals within the Big Data can be recognized and decoded. That’s where machine learning does its part. Machine learning does the hard work for hoteliers, all at a speed the human brain could never achieve. What would literally take years for a person to understand, modern computers running the right software can figure out in moments. The machine sifts through the Big Data, automatically determining relevant and irrelevant data, weighs the importance of each piece of data in the pricing puzzle, and incorporates it all into a forecast revealing perfect pricing.
For example, machine learning can determine there are 40 small events in town on a specific day. Individually, each event has no effect on hotel demand. But, together, those events in aggregate moves the demand needle. This is a massive bit of insight because it reveals all those people that are more likely to pay a higher price for a room because they have to be in that city on those specific days.
Machine learning in conjunction with Big Data inherently understands that people stay differently each time they travel because of minute yet detectable traces in the data. Machine learning evaluates, weighs, and determines the validity and relevance of myriad data points such as integrating up-to-date market data (including supply and demand), room pricing of direct (hotels) and indirect competition (including peer to peer lodging sites such as Airbnb, and other alternative accommodation options available within the destination), review scores on hotel review sites, among many other variables.
Together, Big Data and machine learning provide the most accurate information from which to base pricing on in the entire history of the hotel business. Setting rates is no longer a guessing game. Leveraging the newest tools on the market gives hoteliers the most accurate forecasting data ever seen. The invisible becomes visible and that porridge is suddenly cooked to perfection ever time.
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