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Revenue Managers job will change but not eliminated by AI

Revenue Managers job will change but not eliminated by AI

With buzzwords of machine learning and AI gaining mainstream attention we hear a lot of different viewpoints from the revenue management community. Some embrace the future at hand, others are sceptical and others are concerned about the future of their jobs.

NB: This is an article by Fabian Bartnick, Vice President Asia Pacific & International Business at LodgIQ

At times, we hear comments such as: “…it is meant to “support” us (the RM) but it feels as if it’s (ultimately) replacing us (the RM)”. Some comments take it even further and question the investment reasons: “Are those making the decision (on RM systems investment) really looking at ways to try and reduce head count cost rather than it genuinely being used to support the RM function or is it being used in the wrong way.”.

Others we here are really upbeat and are looking for the next step in the evolution of analytics – “can’t wait before it (RMS) starts talking to me”, “when will it become VR enabled” or even as simple as “the RMS has really helped me make better decisions as well as cutting down all the manual tasks”.

There are plenty of complexities and user cases that can be taken into consideration when looking at RMS, AI, and the future of Revenue Management / Managers. Let me try and address a few of those in its simplest form: The persona of the revenue manager, the buzzwords / technology, and the correlation over time.

The Persona of the Revenue Manager

Revenue Managers have evolved…and so have their counterparts – Sales, Marketing, Operations, etc. The Revenue Manager is not the only one understanding data anymore or the use of analytics.

Equally, Sales & Marketing have seen that others grasp the concepts of their disciplines too in regard to negotiation techniques or digital acquisition. From where I sit: Revenue optimization and marketing optimization are so tightly connected that one cannot live without the other – meaning also: All disciplines must understand each other.

Revenue Managers who are “scared by the system” or plain simply “don’t need one, don’t want one” need to wake up to the reality: RMS have been proven repeatedly that they (through automated analytics) will help you in your decision making and make you, the revenue manager, better as a result of it. It’s not a question anymore about “should we get a RMS?” but “when should we get a RMS?”.

The modern Revenue Managers is a commercial manager. Driving the business, setting the strategies, involving, and collaborating with all other departments to create a united approach for the best of the business, not the best for one discipline (commonly known as Silo).

The buzzwords / technology

Machine learning and AI to a certain extent has been around for many years now. Just because we didn’t hear about it mainstream doesn’t mean that it wasn’t there. The application of machine learning in the revenue management space has also been around for a bit yet, its application and positive outcomes have only been felt since not long ago. Machine learning has its place in modern analytics and thanks to more revenue management companies evolving, we see more adaption of the concept.

Let’s focus on the core of what the technology is all about. At its core, a RMS has one primal function: Optimize Revenue for the hotel (i.e. make you more money) – plain and simple. But even here are vast differences of how this is approached:

1. Out with summary level data and in with transaction level data
Some systems are still using summary level data (not optimal anymore in this day and age), others use transaction level data (which is what you want) to drive its algorithm

2. Machine learning is required when you talk data that hasn’t been touched before
Just using your PMS data, you can get away with using traditional approaches to forecasting. Yet adding Flights, Vacation Rental, Website, Intent, etc. you require Machine learning as those sets are unrelated at times and traditional regression models will not find the connections – Machine learning will.

3. Only use technology that you understand
If it’s too complex it might just be that someone forgot to think about the user or make it complex for the sake of it. At the end of the day, you are the one working with the system and deciding “yes or no” based on the data that is presented to you. No black box, no guesswork – you need to be able to see and work with the data dynamically so you can make the best possible decision. Everything else is playing lottery.

4. Golden rule of thumb: If you buy it, use it
A RMS is not a children’s toy that you buy, play with for a day or so before moving onto the next toy. It is a decision to elevate your company to the next level in its analytical evolution. You are actively taking a step to support your decision makers with technology therefore use it. Too many times technology gets deployed only to be left on the sidelines or for its recommendations to be ignored. You deployed the RMS for a reason so use it for that reason.

We are moving into an era where data is democratized; available at your fingertips. Technology will play its part (an AI, Machine learning in that matter) to take some of the current tasks off our hands to do it faster and better (whoever got mad at excel for that one formula not working will know what I mean) – therefore increased productivity for us Revenue Managers.

Correlation over time

Bringing both together and addressing the fundamental question: Is AI going to replace us (Revenue Managers)? The concise answer is “No, it won’t at this stage” – and personally I don’t think in the near future.

What I do believe though is that both, technology, and the user, will continue to evolve further and therefore we require better systems, faster, more advanced to support the evolving user in its quest for seamless decision making – same as the consumer wants a seamless travel experience on their side.

The Revenue Manager might not be called Revenue Manager anymore but Revenue Management will fall within his or her remit to drive the overall performance of the asset (yes, asset) and all the revenue streams that go with it (even taking it as far back as deciding on the room count configuration based on future revenue opportunities).

Overall, the future is filled with humans and better technology alike. Smartphones, tablets, wireless, VR have all shaped our world over the last decade and with so much innovation happening in the technology space as well as what the user demands I don’t see an end to a symbiosis between technology and the human using it. Let’s embrace it.

Fabian Bartnick is the Vice President of Asia Pacific & International Business at LodgIQ overlooking all aspects and operations within the region as well as the commercial part of LodgIQ. Fabian’s career covers all sides within hospitality including property, regional and corporate level roles as well as consulting and technology vendor roles across 4 continents and 25 countries. Fabian has helped built and apply price optimization, visual analytics and total revenue management tools across the globe and successfully turned around revenue performance in many companies by providing strategic consulting services as well developing powerful revenue analytics solutions that empower the business to maximize its revenue potential. Fabian has been on the Board of Directors for HSMAI South East Asa and currently sits on the HSMAI Advisory Board.

LodgIQ™ provides advanced travel industry revenue optimization technologies. Its breakthrough next-generation revenue optimization platforms, LodgIQ RM and LodgIQ ONE, were developed by seasoned revenue management executives and Silicon Valley technologists. LodgIQ’s products combine sophisticated machine learning with an intuitive and powerful user interface delivering advanced recommendations and actionable analytics. LodgIQ RM is for full service hotels while LodgIQ ONE is geared to boutique, independent and focused service hotels. LodgIQ is headquartered in New York City, and maintains offices in Silicon Valley, Singapore, London and Bangalore.

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