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This is an excerpt from our trend report, From Data to Action: The Future of Hospitality Marketing, brought to you in partnership with Cendyn/ONE.
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As hotels grapple with increasing amounts of data, effective customer relationship management stands to become even more important. Whereas hospitality’s points of contact have traditionally been anchored to registration desks and guests’ person-to-person requests, brands, armed with guest data, are increasingly able to reach out and apprehend needs and behaviors via an expanding array of always-on mobile devices.

Once a chiefly reactive role, customer relations has become a proactive space.

Given the multiple kinds of data that hospitality now gathers from numerous sources, linking disparate information so it can be visualized, analyzed, and acted upon is key to extracting value from it. The value is in the potential endgame for hotel-guest relations — earning repeat business.

Upgrading the hotel-guest relationship: the value and potential of linking data

A key area in which big data can equip hospitality leaders is that of identifying and pursuing properties’ best guest-types.

That is, within guest check-in data, folio data, reservation records, website activity logs, marketing histories (from SEO to paid search and social) there are multiple ways and spaces in which hotels are reaching customers. Patterns that show which of those customers represent the most spend and the highest degree of loyalty represent key opportunities to boost revenue. For some brands, that kind of collection and analysis can lead to surprising discoveries about what marketing and leadership have traditionally thought they understood about their guest demographics.

“Like everybody else in the world, we’ve been trying to get a handle on that Millennial and younger customer,” said Josh Herman, director of Marketing and PR at Fontainebleau Miami Beach. “One of the interesting things we’ve done — as much as it’s a young and hip and trendy image we portray here in general at Fontainebleau — is that a lot of the data we’ve gathered has showed us customers in older demos — in their 40s, 50s, and 60s — really are the customers spending significantly more money on-property.”

It is an insight that Herman said became apparent only after de-siloing the data that existed within Fontainebleau’s system.

Furthermore, the data now at their centralized disposal allows the hotel to better market to guests who are able to afford the property’s higher-end suite product. Nearly 1/3 of Fontainebleau’s inventory is in the form of upper-tier suites. But, according to Herman, an often significant percentage of that inventory was being distributed as upgrades to guests who’d paid for lower priced accommodations. “We’ve changed our marketing message to the slightly older demographic,” he said. “We’ve made it more suite-based.”

By revisiting data in more powerful ways, Fontainebleau identified characteristics of incoming guests and outgoing marketing that run along different lines than prior presumptions. They can market the expensive inventory more directly to the audience that books it at full price. According to Herman, it’s changed the brand’s bottom line for suites at that property.

“We have seen a noticeable difference in the upper-category rooms that we are getting people to pay for versus upgrading for free,” he said. “It’s had a noticeable affect on our rate.”

Starwood, as another example, recently created an engine for assessing and optimizing revenue based on datasets surrounding guest preferences, room inventory, and property location. The technology in place helps its marketing teams reach potential guests with highly tailored incentives to book. In Q4 2014, the brand saw a 58% increase in revenue over the same quarter 2013.2

Big data has been key to guest-pattern recognition and better predictions for Marriott as well. Taking five years of customer satisfaction and guest loyalty data gathered from more than 42 properties, it was recently able to identify a correlation in the way these elements interacted over time.

  • Analysis showed that a 5% overall increase in customer loyalty in a given year created a kind of distillation effect in the next year.
  • Marriott was able to see an average 1.1% in-crease at a given property in the year following the observation of an overall loyalty lift.

“These sorts of measures and relationships can be very helpful to managers in planning ways to increase financial performance over time,” wrote Thomas Davenport in an Amadeus report that included the Marriott example. “With the availability of more external big data, travel industry firms can begin to use other measures of consumer demand to refine their predictive models.”

But data of the kinds employed in the preceding examples can be tremendously spread out. Conversely, when centralized, a set of tools can be introduced so that a property’s teams can work with the assembled information and apprehend new insights from their aggregate. In this approach, the potential opens to create actionable opportunities to reach out — even in advance of guest prompting.

With examples of the value that comes with a linked-data environment in mind, let’s turn to what steps hotel brands might take to affect that value for their own marketing efforts (and toward greater returns). The bridge between the past and present of data in hospitality is one of technology and human expertise.

This report brought to you in partnership with Cendyn/ONE. You can download the full report here for all the insights.
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