There’s a lot of talk these days about the emergence of quantitative analytics and new opportunities to be found in huge sets of raw data. Massive number crunching certainly is a part of our demographic present and future, but for businesses that stake success on one-off customer service, qualitative analysis will always come first.
Nowhere is this more evident than in the hospitality and entertainment industries, where customers quickly come to associate certain brands and facilities with a superior experience. Gaylord Entertainment Company is a leading hospitality and event-planning provider that operates four hotels, each with 1,400 to 2,900 rooms. The latest, Gaylord’s 2,000-room National Resort & Convention Facility, opened near Washington, D.C. just months ago. Gaylord maintains other core assets including the Ryman Auditorium in Nashville and the Grand Ole Opry.
Tony Bodoh is manager of operations analysis for Gaylord Entertainment Company. With an unlikely background in lean manufacturing practices, Bodoh keeps his focus on processes and organizational profitability, having brought them right along to the hotel business.
“As I got into the position I’m in now I realized, like everywhere else, the business has a core set of customers that are more profitable than others,” he says. “And in hospitality, the point is not so much to have a demographic or psychographic profile of customers; it’s about understanding how we are serving them.”
Eighty percent of Gaylord’s business involves conventions, meaning that the average visitor assembles a cumulative opinion of service over a stay of three days. If acres under glass and hundreds of thousands of square feet of meeting space are simply expected in the high-end convention business, they do not by themselves enable a superior customer experience.
An Ear to the Wall
What Bodoh really wanted was rich feedback about customer experiences, and he knew that sources for the information already existed or were planned. These resources include customer surveys managed by a single provider, a dedicated call center to manage discrete requests for services as simple as extra towels, external reservation and concierge services. In a strategy that could apply to any business value chain, Bodoh came to look at service channels as “listening posts” that could contain sources of detailed customer experience information.
Managing, mapping and tapping these resources called for content mining and text analytics, and just this year Gaylord turned to customer experience management (CEM) specialist Clarabridge for a solution that could match its own timeline of planning.
The proof of concept involved taking 30 months of Gaylord’s data, which was then exposed across the Clarabridge toolkit and customized for various reports over three months. Bodoh describes the result as “an iterative format that worked well.” He credits the hosted software delivery model as both a timesaver and a relief to Gaylord’s IT staff.
“At the time we were managing 6,000 rooms and were just about to add another 2,000 in a situation where you basically turn the switch overnight,” Bodoh says. “When the option of software as a service [SaaS] came up, it wasn’t what we were originally looking for, but we saw a great opportunity to take the load off our own staff and we’ve since needed very little internal IT support. It actually turned out to be a big driver in our decision and it’s worked out well.”
Compiling Customer Sentiment
The system is being rolled out point by point, following the model of “listening posts” consisting of surveys, emails, chat, messaging, call centers and online forums used by meeting planners. The initial focus was content capture, beginning with guest satisfaction and then moving on to the individuals who book the resorts.
In the old process, verbatim contents were manually entered into more than 300 categories of customer issues. The new process captures even more categories, but now the system is automated and runs in minutes versus the three to four-week turnaround of the manual method. “We carry a very extensive list of categories, but we’re talking about a hotel and a convention center side by side and three days of experience for a guest, so there is no end to the problems that could occur,” Bodoh says. The categorization process included parameters for where a problem occurred, its nature and who might be responsible for the area affected, assigned to metadata that could be understood and put to work in short order.
Through the metadata, Bodoh was able to categorize about 30 percent of data. Once the machine learning in the Clarabridge software was turned on, Bodoh found he could make use of all but the most obtuse comments. “If you look now at what’s not being categorized, it would be a comment like, ‘Great’ or ‘Terrible experience,’ things you have no way of connecting back to a root cause. Those comments add no value for us because we can do nothing with them to help the guest, but virtually everything outside of those comments is categorized now.”
Timeliness of data gives general managers or directors of operational excellence the ability to act on current issues rather than backfill older problems, Bodoh says. “The other problem was that the old categories only showed us the quantity of a problem,” he says. “They didn’t tie back to the loyalty of the guest or the meeting planner, which was a disconnect in my mind. If there were problems with the quality of a room, I couldn’t tell if that was causing our guests to become disloyal. In the old system we would be assuming that the quantity of data would be a proxy for the severity of a type of problem, which is often not the case.”
Survey data in hand, Bodoh has moved on to Gaylord’s call center and an external reservations system. Internally, Gaylord also maintains a dedicated call center for a program called “Consider it Done,” which jumps on guest requests for towels, business needs or other services. It’s one more listening post Bodoh can mine in order to improve processes so fewer guests have issues.
Across programs, Bodoh is experimenting with filtering and compiling information to feed into the system while identifying key data sources and keeping an eye out for redundancies. “One of our goals is to feed data back from Clarabridge to those listening posts. The goal is over time to get a very integrated solution that not only captures information helps us know how to quickly respond to a particular problem.”
Some preliminary work is under way that will better integrate customer relationship management (CRM) and transactional data in Gaylord’s systems. “We’re hoping to merge two worlds. We’d only gotten into the CRM world in the last year or so, and we’re going to work in the call centers so we can recognize a guest or a former guest, but it’s a work in process.”
The vision is to understand who a customer is, where they are from and expand from there. “These elements will connect to a point where if you understand a customer had a problem in the past, we would be able to understand how we would better be able to market to that customer or to a group of consumers like that person. It’s taking the typical CRM applications and using psychographics and demographics along with the data from 10 or 20 percent of customers who have given us a response to help us understand what they’re really saying. We can apply that to the entirety of the demographics and psychographics on top of that, real experiential stuff that has happened at our hotels,” Bodoh said.
All this keeps the whiteboard busy for Bodoh and Gaylord. “I would say the biggest challenge is to focus on the two or three things that are our fundamental foundation or can make a change in the way we operate. I think we’ve identified those with some help from Clarabridge; now it’s come to the point of getting engaged and resolving those issues and tracking how we’ve done compared to the baseline. It is a little squishy to talk about sentiment, but qualitative measurements tell you a lot about expectations and success. With years of history in the system we’ve begun to understand that if we move one lever, it will impact the business.” Over time, the hope for Bodoh is that Gaylord will be able to study and understand what is likely to happen when a particular lever is pushed.
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