Four Ways to Use Big Data in Hospitality


A hospitality business has countless ways to collect snapshots of consumer data—past travel bookings, seasonal sale trends, and more. But without looking at all of the data together, the business is missing key insights.

That’s what Big Data is all about—taking a company beyond the snapshots of individual consumers and singular moments to get a more holistic view of a target market. Travel consumers are an especially tricky bunch for marketers because they not only vary widely in demographics, but one individual consumer can have different reasons behind each travel experience, resulting in different behaviors and expectations on each trip.

Bernard Marr sums up the challenge for hospitality marketers in a recent Forbes article: “A customer’s lifetime value might not be empirically obvious from observing their behavior during one visit.” Assessing the lifetime value of a consumer requires looking at their behaviors from a much wider angle to encompass more information over longer periods of time.

Hospitality marketers, in particular, should take advantage of Big Data to guide their efforts as they seek to reach diverse groups of travel consumers with widely fluctuating desires, needs, and expectations for travel experiences. These four tips are a guide for how to use Big Data to propel hospitality marketing forward and reach big picture goals.


1. Categorize Customer Types
Travelers are too widely varied for one marketing strategy to effectively connect with all of them. The successful travel provider, hotelier, or restaurateur picks a primary market and masters it. Collecting and analyzing Big Data will help capture those travelers who are perfectly suited for specific products and services. The traditional business traveler on a budget looks for a comfortable room, convenient dining, and fast and reliable internet access. The bleisure traveler, on the other hand, is looking for the same budget-friendly amenities with added entertainment and luxury to pursue after their working hours. The average family traveling with young children wants a low-cost, clean, and child-friendly facility. The honeymooning couple seeks to splurge on luxury.

Customers come with a myriad of expectations, from those who just want to grab a bite or a night’s sleep and be on their way, to those who want to be thrilled by adventure or enveloped in luxury. Determine which specific travel consumers fit a business and use Big Data to identify the best opportunities to reach those people, draw them in, and exceed their expectations. This will also help to better identify one-time guests who are unlikely to return and those customers with a higher overall lifetime value so hotels can focus more time and resources where they’ll have the most impact.

2. Find the Highs and Lows
Analyze Big Data to identify peaks and dips in a business cycle, and use that as a guide to set appropriate prices for goods and services.

Savvy airlines, cruise lines, hotels, and others in the hospitality industry can use this information to their advantage to fill empty spaces for the best possible net profit. For instance, a cruise ship that charges its highest rate yet sails at only 25 percent capacity loses money overall, compared to the ship that sails at 90 percent capacity with lower paying customers. The latter scenario creates a positive ROI, meaning more economical staff-to-customer and resource-to-customer ratios. The ship’s going to burn the same amount of fuel traveling from port to port whether it’s carrying a full load of passengers or not.

Consider the improved customer experience that comes from adjusting pricing to fit demand. The cruise line with more passengers who paid an affordable rate will likely see repeat customers and receive word-of-mouth recommendations. Those consumers who enjoyed the trip with the other cruise line, but feel like they paid too much for the value received, may not use that cruise ship service again.

3. Cater to Relevant Customer Needs
Big Data analytics doesn’t just focus on existing guests’ habits—it takes into account the catalysts that cause people to become guests. Capitalize on Big Data to anticipate travelers’ needs in a timely manner, and tailor efforts to deliver to those who most need specific services during a given time period. This often means looking outside of internally-collected data and analyzing external factors such as sales patterns for related hospitality businesses or consumer opinions of competitors.

In his Forbes article, Marr provides the following example using economy hotel chain Red Roof Inn. When flight cancellations hovered around 3 percent, tens of thousands of passengers were left stranded every day. The hospitality company used data on weather conditions and flight cancellations to target customers who would find themselves searching for overnight accommodations on their mobile devices, and as a result, saw a 10 percent increase in business.

Then, there’s data that goes beyond raw numbers. Qualitative data relies on customer feedback retrieved from customer reviews, social media, travel sites, and front desk personnel. Survey a group of target consumers to determine whether a hotel’s services are actually fulfilling guests’ wants and needs. The information collected can shed light on the traits that customers value and the issues that drive them away, indicating which services and operations hoteliers might consider adjusting. Those changes don’t have to be costly. Some can be as simple as enabling hotel housekeeping, kitchen, or banquet staff to anticipate a guest’s needs and respond accordingly. The ability to anticipate and cater to customer needs results in those personal touches that yield higher reviews and increased business.

4. Ask the Right Questions
The “right” questions are those that probe beyond the obvious. Deeper analysis into what might appear to be unrelated information can yield insights that target a specific customer niche and increase sales. One way to check that the right questions are being asked is to first define the type of data analysis. There are three common types of data analysis: descriptive, predictive, and prescriptive.

Descriptive analysis focuses on past trends. This often shows evidence of results following changes in action, policy, or property. For example, did adding a vegan menu really increase sales? Descriptive data helps a restaurateur determine that.

Predictive analysis focuses on what is likely to happen in the future. The very nature of forecasting the future adds an element of uncertainty that business executives may not welcome. However, this type of analysis offers valuable insight, especially when combined with information yielded from descriptive analytics.

For instance, predictive analytics may signal a peak season near the end of March for spring break, which would then incite increased competition for hotel rooms in certain popular vacation destinations. That knowledge may result in a hotel adjusting its rates to take advantage of the anticipated influx of customers.

Prescriptive analysis involves advanced algorithms that process Big Data to suggest possible actions based on future occurrences. For instance, booking engines can personalize the online customer experience by using past data to predict a consumer’s future needs, and then suggest and deliver a customized vacation package.

Big Data is widely available to any hospitality business—from internal systems to publicly accessible forums. Once a business has centralized and integrated the Big Data needed, it can adjust accordingly to categorize a target market, reach consumers with the highest lifetime value, and exceed consumer expectations by anticipating and meeting their needs before they’ve even had time to ask. Isn’t that what hospitality is all about?


About the Author
Roseanne Luth is the founder and president of Luth Research, a privately held market research company founded in 1977 and located in San Diego, California.

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