The Advantages and Challenges of Lean Revenue Management, with Michael McCartan

Michael McCartan, a Chief Growth Officer for Atomize chats with Robin Trimingham, The Innovative Hotelier Podcast Host regarding the key differences between a legacy revenue management system and an open AI network ecosystem for predicting demand and price setting.

Driven by the need to find new ways to operate with less staff, and simultaneously free staff up to focus on higher quality interactions with guests, AI systems are being introduced throughout the hospitality industry perform many functions best done by machine learning. Highlighting the many lessons learned during the pandemic, Michael discusses the extent to which Atomize’s recent partnership with Trivago will enable participating hotels to increase revenue because it analyzes the data collected when people search for information on destinations and properties and then automatically sets prices appropriately – presently the right price – to the right guest – at the right time.

Highlights from Today’s Episode

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Episode Transcript

Robin: Welcome to the “Innovative Hotelier Podcast” brought to you by “HOTELS” Magazine. I’m your host, Robin Trimingham. And today my guest is Michael McCartan, Chief Growth Officer at Atomize. And today we’re chatting about, “The Advantages and Challenges of Lean Revenue Management.” Welcome, Michael.

Michael: Hi, Robin. It’s great to be here. Thank you very much for having me.

Robin: Well, I see that congratulations are in order for Atomize’s recent partnership with Trivago. But before we get to that, let’s start by explaining to everybody, what do you mean by lean revenue management?

Michael: Yes. So if we take a step back, the original objective of revenue management was to provide the right price to the right guest at the right time. And that was the role of the revenue manager. But over as the years evolved, more and more tasks were put on the revenue manager and, you know, they had more and more things to do. And they started questioning what was important, what was necessary, what was essential, and they sort of lost sight of the key function, which was to price correctly for the right guest.

And this situation was actually made worse by the pandemic when large numbers of revenue managers were laid off. They were one of the most affected groups of people within the hotel operations. So lean revenue management is really going back to that key principle of saying, “These are the key tactical responsibilities of a revenue management, i.e, to set those prices, but they’re best done by intelligent applications, by machine learning or AI, if you will.”

And by doing so, that allows a hotel to operate more efficiently and leave the people that are working in the Revenue Management Department to focus on more strategic things. So they can spend much more time on things that really matter rather than deciding whether they charge $130 or $131 for a hotel room on a particular day.

Robin: You know, I’m sure to some people listening, the difference between a dollar per room per night doesn’t seem significant, but if you have a hotel with 500 or heaven forbid, 1,000 rooms, I mean that mounts up day after day extremely quickly. So, talk to me here, why does traditional revenue management’s heavy reliance on the use of historical data for forecasting present a challenge?

Michael: Well, I think we just have to look at the pandemic. When all previous trends and business behaviors changed overnight and fundamentally. So any hotel that was relying just on past performance and trying to replicate what happened in, let’s say, 2019, and project it onto 2020, clearly was going to be in a difficult situation.

But the historical data is what most revenue management systems use in order to determine price. I like to think of it almost like, if you’re driving a car and the windscreen is completely muddied over and you can’t see forward, the only way that you can navigate ahead of you is to look in the rearview mirror to see what’s happened behind you.

That’s essentially what revenue management systems have been doing for decades. Suddenly, you know, and as you can imagine, as soon as the road ahead changes all the potholes or curves in the road that you hadn’t anticipated, you’re in for a lot of trouble. And that’s essentially what happened with the pandemic. The road ahead was fairly predictable. So looking in the rearview mirror was an acceptable way to price. But as soon as the road ahead changed and there was all sorts of diversions and disruption, that method no longer became relevant. And that’s a severe shortcoming of traditional revenue management.

Robin: You know, I think you’re making an excellent point here, which I guess to most of our listeners is probably painfully obvious by now. What have been some of the biggest lessons learned from all of this from your perspective?

Michael: I think the most important lesson that I can see that’s happened is that as hoteliers and as working in this industry, we learned to say that this is a people business, and that is true. But the reality is that a lot of the people that are employed in our industry are doing mundane and repetitive tasks. They’re not very fulfilling roles. And what happened as a consequence, you know, the big labor shortage is suddenly we don’t have the staff available anymore to fulfill these redundant and repetitive tasks, and basically, fairly unfulfilling roles.

So, hotels have had to think about using the resources that they do have in a more meaningful way. And I think there’s been a recognition that technology, and specifically, intelligent technology, can really automate those redundant and repetitive tasks, do a lot of the doing that previously people had to do, and leave the people that are left behind to actually do more creative, more innovative, more human things like engaging with guests when it really matters rather than just…

It pains me when hoteliers tell me that, “All guests like to come in and be greeted at the reception,” which may be true, but not if it’s to say, “Have your stage had before, can I have your passport details, etc, etc,” that is something that should be automated. And the pandemic has really forced hotels to reconsider those friction points in the guest journey, not only in the guest journey but indeed in the back office as well. So I think that’s the biggest lesson, is that we have fewer people and we need to empower those people to provide more value, give them more fulfilling roles, and actually create better guest experiences for the customers that walk through the doors.

Robin: You know, I have to agree with you. I’m in an interesting position that I get to chat with everybody from all sides of the hotel and hospitality industry. And what I’m hearing repeatedly at the moment is that it’s all about better quality, more meaningful guest experience from the guest’s perspective. And, you know, something like this can really free people up to pay more attention to the people that they’re serving.

So, when I was doing a little bit of background research on you and preparing for our discussion today, I came across what I think was actually a fairly prophetic article that you wrote in, I think it was the beginning of 2020. And you concluded that hotel companies had unwittingly disadvantaged themselves against the OTAs by not being more collaborative in the sharing of data. So how does this highlight the value of Atomize’s partnership with Trivago?

Michael: Yes, it’s interesting. For decades, technology companies created what I’ll call, ‘world gardens.’ These little ecosystems that existed entirely on their own. Little world that existed entirely on their own. And all the data that they gathered, whether it was guest data or reservation data, or any manner of data, they considered it theirs and they limited access to anyone else. So, what happened is these…it was almost self-fulfilling and it was self-preserving in a way that by owning this data, they excluded anyone else coming in and competing with them.

But what it meant is that all innovation was essentially stifled. So, if there was a new application that was going to enhance the way that the hotel operated or enhance the guest experience, that new application rarely wasn’t given any oxygen to thrive because the data is the oxygen. So by creating these barriers, by creating these walls around that data, it rarely killed any innovation.

What’s happened, fortunately, and with what we call the open API movement, the more modern systems talk to each other, and now it’s a networked ecosystem with the free exchange of data, and that allows hotels and the technology partners that support the hotels to share all relevant information, thereby improving not only the operation of the hotel itself but also the guest experience.

Because suddenly, you know, for example, if you came to the hotel, they would know who you are, they’d know a little bit about you, they’d greet you by name, they could automatically check you in without you having to share your passport details, etc, as I mentioned beforehand. So that’s removed a friction point, created a better experience for you, and the person serving you can actually engage with you on a more human level, rather than just being robotic in their questions and answers. So, yeah, by creating this ecosystem, it’s allowed innovation to thrive.

Robin: Okay. So, obviously, that sounds really good. Pretend for a moment that I am a somewhat skeptical hotelier, convince me, how will having access to this data help hoteliers better understand future demand and increase revenue with less resources?

Michael: Yeah. So if we look at the Trivago example specifically, and from Trivago, we’re getting this forward-looking data, so call it traveler intent, because what will happen is people will shop before they book and Trivago is collecting a lot of information about demand for a certain destination. So, if your hotel is in that destination, we are collecting a lot of information or Trivago is collecting a lot of information around that intent, which they’re now serving to us.

So, we are able to anticipate and model that demand and increase the hotel’s prices automatically without them having to sift through reams and reams of data trying to detect those patterns themselves. So, by ingesting the information, responding to that information, we can drive the hotels prices higher in response to demand that they haven’t even seen yet.

They may not have actually received any reservations for that particular day, but the demand is there. And as you said earlier, you know, a $1 increase in price across multiple, you know, hundreds of room types, add significantly to the top line, which actually flows right down to the profitability of the hotel.

Robin: So I’m gonna try asking an extra question here. Does this mean that you can have more immediate tying of traveler’s searching and taking interest in properties and destinations and the resetting of the prices?

Michael: Absolutely.

Robin: Wow.

Michael: It’s like the windscreen example. Suddenly the Trivago data, that forward-looking data, is almost like windscreen wipers. It clears that muddied windscreen, allows you to look at the road ahead, you can navigate around the potholes, you can choose a different route. If the route that you were following is blocked, you can, you know, choose the alternative route and capitalize on, or ensure that your business objectives are achieved because you now have foresight and visibility into the future.

Robin: Fascinating what’s happening in our industry at this time, and makes me wonder where we’re gonna be in a couple of years. We’ve got a minute or two left here, Michael, what’s your key message for everybody who hears this recording?

Michael: Yeah. So I think, and it’s a theme that I’ve spoken about and I’m a technology evangelist, so it probably comes as no surprise. But really, I think my key message to any hotelier or any person involved in hotel operations, is to look for the friction points in their operation. What are those manual processes? There’s mundane processes that A, are creating uninteresting jobs for people, but B, would improve the efficiency of the hotel operation.

And once you’ve identified those friction points, identify technologies that allow you to intelligently automate those processes. A good example would be a group inquiry. If someone phones up to a hotel and says, “I’ve got X number of people looking to book.” In most hotels today, a very manual process with a lot of paper being shunted backwards and forwards between different departments.

That process is ripe for automation, intelligent automation, where machines can read the inquiry, respond to their group inquiry with minimal or no human intervention. And that frees up those people to actually go and engage with guests and think, and create, and innovate, which is, and ultimately create better and more fulfilling jobs for the hotel staff.

Robin: Well, Michael, thank you so much for your time today.

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