Machine Learning, or Predictive Analytics, is in common use in the travel and hospitality industry. Online travel sites such as Expedia, Priceline and Trip Advisor have been putting data to use for over a decade, and they are very good at selling us stuff with Machine Learning. They start by figuring out what someone will pay (relative to other customers) for a travel service, classifying preferences for all of the travel services they have available to sell, and recommending the next thing a website visitor need. Flying to Vegas you say? It’s very likely a hotel or car rental is in your future. But which hotel offers will keep you on their site, buying from Expedia and not Expedia’s competitors? Do you expect a premium hotel experience? Do you need a Fiat or a GMC Acadia? Are you a fan of Blue Man Group, Celine Dion, or Penn and Teller?
And they usually get it right! They are using what is called a Recommendation Engine in order to guide customers through a series of buying decisions, based upon patterns and preferences which are similar to other customers. Underneath, it’s just math, but the impact is undeniable. It enables companies to predict what their customers want, and then (this is the critical piece) anticipate and deliver a response to that want, at a time when a customer truly needs it and is willing to pay for it. Right thing, right place, right time.
Recommender is one of four core Machine Learning problems. These four problems represent what ML can solve. Now let’s apply Machine Learning to another business model within the Hospitality vertical: Hotel Management. Is machine learning relevant to hotels? If an algorithm can anticipate a guest’s need, what can hotel staff do with that information? The answer is: create a customer for life.
One common misconception about Machine Learning is that, in order to produce any useful insights, you need tons and tons of data (or, Big Data). Property Management Systems (PMS) used by hotels and hotel groups are proving this wrong: they are using ML on a small scale to manage revenues, improve profitability and delight their customers. Here are three examples of how hotel and property management groups are using Machine Learning to manage revenue today.
Customer segmentation is not a new thing. We understand the difference between family vacationers and business travelers – those are big segments. But Machine Learning goes further, helping companies discover segments they may not realize existed. Which customers want to be near the pool, and which ones need three morning papers before they can even get dressed in the morning? Armed with this knowledge, hotels understand what matters to guests, at the individual level, enabling them to anticipate their needs immediately. Even more, hotels can understand key characteristics of their most profitable customers, and recognize the next one when they login to the online reservation site.
What kinds of services will be in demand throughout the year? Can a hotel optimize room offerings to suit changing demand patterns, and perform renovations or scheduled maintenance in a way that has no net impact on profitability? While hotels may have limited ability to grow or shrink their inventory of rooms, management can optimize resource needs in order to respond to predicted demand levels.
Airlines understand very well that different customers are happy to pay different prices for the same airplane seat. The same is true for hotel rooms. Often, profitable customers are ones who are least price sensitive, but some are quite price sensitive — choosing to spend their money on other hotel services offered.
Machine Learning may seem intimidating, especially if you are assuming you’ll need Big Data and massive computing power in order to yield an investment. Actually, the key ingredient is knowledge of your industry, and what you, as a services provider, can offer customers. Most hotels and hotel groups have enough core guest information to begin deriving valuable insights and begin competing for customers in the new world of data