Impact of machine learning on hotel operations

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Concrete examples of machine learning technologies that reshape hotel operations
It is now becoming essential to understand the impact of machine learning on hotel operations. That, along with its potential and limitations. This is especially relevant now, with the growth of big data and artificial intelligence in your hotel tech environment.

Our world is fast evolving. You need your hotel technology to grow and also, the ability to learn and adapt from experience. That’s the promise of machine learning. The large availability of data has enabled us to create algorithms that leverage information in real time and makes decisions automatically.

For hospitality, machine learning was first used to fuel hotel staff productivity. In other words, this was done by automating repetitive and relatively simple decisions. It is now evolving towards customer-facing applications, changing hotel marketing and customer relationship irreversibly. What is the next step? And what will be the place for humans in the next couple of years?

Machine learning fuels the productivity of hotel staff

PriceMatch was my first experience with machine learning applied to hospitality. Independent hoteliers were tearing their hair out about how they should price their rooms. We created a revenue management system that could make real time price recommendations. As it happens, algorithms were better than humans at picking current trends such as customer behavior, competitors, pricing and so on.

They were also more adept at forecasting demand and suggesting an optimal price. With a bit of machine learning and smart algorithms, hoteliers could save valuable time and increase profits. And there is so much to do in a hotel that saving 1 or 2 hours per day was a huge win. Thus, this shows the huge impact of machine learning on hotel operations. Hoteliers are not less occupied than a few years ago, they just are more productive.

Machine learning reshapes hotel marketing

Today different kinds of online visitors with different expectations visit your website. However, you show them the exact same standard information. From the customer’s perspective, you are but one option among many. Your challenge is to become relevant immediately. That’s why your ability to adapt and personalize the online experience has become so critical.

E-commerce giants such as Expedia or Booking.com etc. have been using predictive and machine learning algorithms to optimize customer conversion for years. Booking.com CMO tells you everything about it here: https://youtu.be/vonnRCIJXxk?t=80

The current frontier of machine learning is no longer to automate painful tasks. In fact, it is to create a competitive edge and perform customer facing tasks that human beings could never do themselves. Thus, ignoring these techniques is like running a 2019 Formula 1 race with a car from the 1950s.

Yes, Mike, your website is beautiful but we just don’t make them like this anymore!

The main difficulty for hoteliers are:

1 – They are no data scientists

2 – They don’t have enough data to crunch

However, some concrete solutions exist, which can help them overcome these difficulties. They can explore the impact of machine learning on hotel operations and leverage this. Here are 3 solutions immediately relevant for groups and independent hotels:

  • Hotelchamp autopilot: Triggers contextual visual messages according to customers’ behavior. After years of analysis, they were able to accumulate enough data to make a few observations. They now know how to define and recognize specific customer segments and trigger messages automatically in an effort to increase direct bookings: https://www.hotelchamp.com/
  • Allora by Avvio: Uses machine learning and a powerful website recommender engine to personalize the brand.com experience for each website visitor. Allora is designed to understand the online signals for each visitor (family vs corporate, international vs domestic, browser vs booker vs pre-stay guest etc.). This adds a personal touch in every scenario to improve direct bookings and drive Avvio’s 25% guarantee. https://www.avvio.com/, https://www.allora.ai/
  • Zoe by Quicktext: AI-powered the best hotel chatbot trained to engage online visitors 24/7 in 8 languages. Zoe is designed to reassure and convince them to book direct, by making the experience as natural as a WhatsApp conversation: https://www.quicktext.im/

What is the place of human beings ?

Automation powered by machine learning is already here. Hoteliers have no choice but to accept it. However, they can choose how they want to leverage it:

  • Cut operational cost (do the same with fewer resources): This option makes a lot of sense for economy segments where customers are looking for price more than service. When I traveled to Meininger Copenhagen, I received an SMS telling me when my room would be ready, my check-out time and a code to enter the room. It was absolutely fine.
  • Improve service (do more with the same resources): It doesn’t have to be a contest – human vs. machine. There is just a race for better service. While machine learning is progressing, the human touch is also becoming increasingly important. Because our expectations adapt and people just expect more. So automation is also the opportunity to deliver enhanced customer service in places you couldn’t go before: Live chat, WhatsApp.

Finally, with smarter machines, maybe the key criterion for a front desk hire will no longer be his proficiency with Opera PMS. On the contrary, it will be about his people skills because the rest will no longer matter. With machine learning, you will no longer need receptionists, you will need welcomers.

If you’d like to ask questions or discuss the impact of machine learning on hotel operations, please visit our website or feel welcome to contact Benjamin Devisme at bde@quicktext.im.

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