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Hoteliers don’t always consider data to be a fundamental tool in their daily operations. Hotel data focus on making guests happy than crunching numbers. So why does data matter to hotels? Because at the end of the day data is just information. When we look at the right data in the right way, we can not only ensure guests are having a great stay, but hotels can optimize revenues, occupancy, and organizational efficiency.

Hotel Big Data

Often spoken about in mythical terms: “with good data anything is possible” or “just look to the data for answers.” Data is vitally important to any modern business, but how you look at your data and what you decide to do with it makes all the difference.

Today, any hotel with a website or some OTA or social media presence already has access to amazing quantities of data. Google Analytics can tell you what’s happening on your hotel website. Facebook can help you interact with customers. So the question today becomes: How do you look at your data?

Let’s take a closer look at the different types of data and analytics hotels use every day.

Big Data and Hotels

Though it is one of the biggest buzzwords in technology, big data is a term that is often misunderstood or misused. But when so many different definitions exist, it’s understandable that there’d be some confusion. At the core of it, big data is a large set of data that, through analysis, can reveal trends. When we say large, we mean large. Thousands or millions of data points are common, so this information will almost always come from sources external to hotels. Good examples of big data are weather, traffic, or social media data. These are giant pools of information that you can sort and filter to find the information relevant to hotel goals.

Small Data for Hotels

While it doesn’t get mentioned as often as big data, small data is just as valuable. In fact, in the case of hospitality small data is often more valuable to hotels operational decisions. Small data exists on a more manageable scale, generated by internal resources like your PMS, channel manager, or website. When properly structured, this data becomes actionable information that can make an immediate difference in anything from your ADR to occupancy.

Structured Hotel Data

Once we’ve covered scales of data (Big and small), we should take a look at the two ways data is compiled. The first is structured data.
Structured data is data that is organized, labeled, or categorized. Think of it as a tag; a streamlined way to filter out the data you want from data that you don’t want. Let’s take a look at reservations, For example. When the front desk adds a new reservation in hotels PMS, they add descriptive information on the guest: arrival and departure dates, name, and country. Because this data is sorted, or structured, you can easily filter by dates, for instance, to understand booking trends.

Unstructured Hotel Data

The other way data can be presented is as unstructured data. If structured data is data that is organized, unstructured data is data that is disorganized. Quite often, unstructured data still has the potential to provide us with valuable insight for hotels operations. The problem with unstructured data is there’s no easy or scalable way to cull the needed insight from the data in its current state. Let’s look at an example. Customer Reviews on TripAdvisor, whether hotels like them or not, it can influence the decision-making process for prospective guests. Yet hotels try to analyze this data, there’d be a lot of manual effort, with little actionable info. That’s because TripAdvisor customer reviews are not structured to be easily sorted.

Hotel Analytics

So there’s big and small data and it can be both structured and unstructured. But once we have it, how do we understand it? And how do we use that newly-acquired insight to make better decisions at hotels? This is where analytics comes in.

Descriptive Hotel Analytics

The most common and traditional form of analytics is descriptive analytics. Descriptive analytics is used to describe past trends based on large groups of data. Descriptive analytics is used in everyday hotel operations in things like performance reports or pick up reports.

Predictive Hotel Analytics

The next step beyond descriptive analytics in both scope and complexity is predictive analytics. Predictive analytics uses large groups of data from the past to not only determine past trends but to determine what may happen in the future. Because predictive analytics is an educated guess, it can never be one hundred percent certain. Still, good predictive analytics can offer valuable insights into a hotels management strategy. A great example of predictive analytics is when you analyze past booking trends to predict next weeks’ occupancy. For those who are in hospitality, they might be more familiar with the term “forecasting,” but it’s really all the same.

Prescriptive Hotel Analytics

The most complex and most recent type of analytics is prescriptive analytics. Prescriptive analytics combines structured and unstructured data (remember those?) with incredibly powerful computation, known in the data science world as “machine learning.” This newly developed technology involves advanced algorithmic processes that basically predict the future based on past performance (like predictive analytics). Yet prescriptive analytics goes further by factoring in big data like weather, traffic, or even geopolitical events. It then offers possible actions based on this data.
Prescriptive analytics may very well be the future of hotel analytics. In fact, some advanced revenue management systems are already headed in this direction.

The hotel and hospitality sector cater to millions of travelers every day, and each one of them checks in with their own set of expectations. Meeting those expectations is the key to getting people to return, and increasingly hotel and leisure operators are turning to advanced analytics solutions for clues about how to keep their customers happy.

Additionally, although their marketing departments would be loathing admit it, not all guests are equal in the eyes of hotel and leisure operators. Some will simply check in and check out with a minimum of fuss. Others will spend hundreds or thousands of dollars on fine dining, entertainments, sports activities and spa treatments. Identifying those customers with a higher overall lifetime value to a business is hugely important in today’s market, but a customer’s lifetime value might not be empirically obvious from observing their behavior during one visit.

For example, a high-rolling customer spending money like it is going out of fashion in the hotel-casino may be on a “holiday of a lifetime” following retirement, and unlikely to behave in this way every day. Meanwhile, a frugal business customer taking an economy room and spending very little on extra services may be a traveling businessman who will potentially return frequently if the hotel meets his needs, and therefore have higher lifetime value. Big Data analytics can help make this distinction.

A third overarching use of analytics in the hotel industry revolves around “yield management”. This is the process of ensuring that each room attracts the optimal price – considering troughs and peaks in demand throughout the year as well as other factors, such as weather and local events, which can influence the number (and type) of guests checking in.

Analytics has applications in all these areas and although the hotel and hospitality sector has lagged others such as retail and manufacturing in adopting Analytics- that could be starting to change.

hotel data

Hotels are now using big data and analytics

One pioneering example included US economy hotel chain Red Roof Inn who, during the record-setting winter of 2013/2014, realized the huge value of having many hotels close to major airports at a time when flight cancellation rate was around 3%. This meant around 90,000 passengers were being left stranded every day. The chain’s marketing and analytics team worked together to identify openly available public datasets on weather conditions and flight cancellations. Knowing that most of their customers would use web search on mobile devices to search for nearby accommodation, a targeted marketing campaign was launched, aimed at mobile device users in the geographical areas most likely to be affected. This led to a 10% increase in business in areas where the strategy was deployed.

To Know About EzDataMunch Hotel Analytics Capabilities

Please Visit : https://ezdatamunch.com/industry-solutions/restaurant-hospitality

Patrick Jaggi

Patrick Jaggi

Business Development Manager | EzDataMunch

Patrick is working as Business Development Manager at EzDataMunch – to prospect for new clients by networking, cold calling, email marketing, set up meetings between client decision makers and company’s practice leaders/Principals. Also, developing ways to improve the customer experience and build brand loyalty.
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