Mining for Big Value in Big Data

By Dorothy Creamer, Managing Editor | February 12, 2014

Big data has been trending for the last 18 months with buzz-word status, but the concept itself is not new. Matt Klyman, principal at Klyman & Associates and a consultant for Au Bon Pain (www.aubonpain.com), explains that the first use of the term, as it applies today, came back in the 1990s from John Mashey, chief scientist at Silicon Graphics. Mashey used the phrase to suggest that there was occurring not only a proliferation in the type of standard business information, but the beginnings of a continuous stream of these different types of data as well.

Today, big data is often summarized by three Vs: variety (the wide range of data types), volume (quantifying the sheer amount of data) and velocity (the speed of data in and out). The ability of systems to handle this trinity while divulging actionable data is the “holy grail” for operators and the modern fascination with big data has reached a frenzy due to the potential value in often untapped resources. With competition becoming more intense than ever, organizations have been searching for ways to differentiate themselves by diving into the wealth of information to improve competitiveness, efficiency, profitability and more.

“In the past few years, big data has demonstrated the capacity to make more informed and timely predictions of market trends, save money, boost efficiency and improve decision-making in the hospitality industry,” notes Juliana Lim, senior marketing director, for Pizza Hut (www.pizzahut.com).

Knowledge is power and profit
When Pizza Hut wanted to learn more about its thousands of customers, the restaurant teamed with Capillary Technologies (www.capillarytech.com). “We wanted to unlock insights from our customer data across various channels and integrate that data to build profiles for our customers, so that we can market to them more effectively,” Lim explains. “We needed a partner who had the capacity to integrate all the massive amounts of data from the various channels, make sense of it and then leverage it to help build marketing strategies for our product launches and new campaigns.”

Pizza Hut’s customer relationship management (CRM) strategy matured to include Capillary Lifecycle Marketer-based campaigns and solutions that are used to deploy hundreds of personalized campaigns across different customer segments. The campaigns integrate Pizza Hut’s data with analytical models for consumer behaviors with corporate positioning for the Pizza Hut brand.  “Even with the large amount of data in place, we were able to maintain a single view of all the customers and extract data and intelligence on demand,” Lim reveals.

Business trends and analytics were a top concern for My Fit Foods (www.myfitfoods.com), a take-out operation which focuses on pre-packaged meals for health-conscious consumers. “We didn’t have anything above-store,” recalls Phil Crawford, CIO, explaining that CTUIT (www.ctuit.com) gave the company the ability, through customized writers, to target certain factors that helped improve business performance. “That was the biggest thing. So much data comes off these things it’s unbelievable, but unless you know how to refine it and retune it where it becomes actionable data, it’s worthless.”

Crawford now produces a daily flash report that has key metrics that operation managers want to see. “If they want to drill down further for additional data it’s all there through their portals,” he says. “It’s quick and to-the-point. They get what they need and then they go back to running their stores.”

The Strand Development Company (www.stranddevelopment.com) has more than 50 hotels in its management portfolio and the company is one of the few major operators to focus entirely on third-party management.  With such a broad range of ownership, the company turned to Aptech (www.aptech-inc.com) to help organize data and better report operational issues. “As a true third-party management company, we have multiple owners and multiple types of owners from mom-and-pop to large publicly traded properties,” says John Johnson, CFO. “We recognize that they have different needs.  There is certain data that some don’t need access to. The system allows managers to run ranges of data and is customizable ­—- not necessarily cookie cutter. To have a cookie cutter type of system that everyone accesses wouldn’t serve them at all.”

Additionally, Johnson recalls that in the past, managers were not able to get reports and statistics until the end of the month. This was fine for historical data, but wouldn’t allow for managers to do anything prospectively. “Now we can do trend analytic projections and forecasts both financially and operationally to meet goals and to report information directly to the ownership groups,” Johnson notes. “Aptech provides a roadmap early in the month to see what we need to do financially and operationally in order to achieve the goals we had set.”

The loyalty-data connection
Frequency programs and social media sites have emerged as powerful conduits to farm for data. Klyman views big data in three parts: standard business data; quantitative data such as inventory levels; and qualitative data coming from social media.  “Before retailers picked up on this, the financial services companies were well ahead of this curve if anyone was. They were householding data years before retailers were. They were early adopters in knowing more about customers’ lifestyles,” Klyman explains. “[Consumers’] retail spending, as a percentage of revenue, is substantially higher than on the restaurant and hotel side. That segment had the financial wherewithal and was an early adopter on the social media side, storing a lot of qualitative data.”

Klyman now sees hospitality and foodservice using frequency programs as ways to find and place a value on data, not just at a gross level, but on a more detailed level as well. “Managers can use frequency data to figure out how customers who come in once a month differ from those who come once a year.”
Strand Development Company also tries to pull as much data as possible from guests’ social media use. “Each property has a Facebeook and Twitter presence and we can process information through those feeds,” Johnson says. “We use that information to market to 80%+ of our guests. The loyalty aspect and finding these people pays dividends down the road.”  Strand is investigating leveraging Aptech’s integration capabilities to pull financial market data, guest service data and guest comment data together so that when regional managers are looking for information on a property they get a full view all in one place.
 
Big data without the big budget
One of the pervading concerns preventing many hospitality companies from investing in big data programs is that big data comes with a hefty price tag. Aashish Chandra, former DVP of Sears Holdings, (www.searsholdings.com) believes this is due to a lack of awareness. “Many people think big data means big budget, but it doesn’t have to be,” he asserts. “Because of the tools available, operators could actually shrink budgets by as much as 50-80 percent.” Chandra describes that some of these tools are very advanced techniques that are open source. “Instead of buying a million dollar machine, use commodity hardware and put open source software like Hadoop on it for a fraction of the cost,” he recommends. “You will be able to run the same amount of data with much better performance for a fraction of the cost.”

A proliferation of apps has also opened doors for many companies to experiment with big data. “I think you are starting to see a shift,” says Steve Heeley, CEO for Earl of Sandwich (www.earlofsandwichusa.com). “Part of the reason you saw larger companies get in the data game early on was because they had the resources to set up these very expensive, customized loyalty programs.” Heeley contends that these huge investments were why early on, only companies like Starbucks were able to implement big data strategies. Earl of Sandwich has deployed the Punchh (www.punchh.com) mobile loyalty app and has been using the tool to pull data from social media and loyalty members. “The reality is, now with mobile and app-based programs, you don’t have to have big investments in infrastructure and management,” Heeley explains. “Everything is intertwined and it has leveled out the playing field. In some ways it is more effective.”

Often what operators find overwhelming is the storage of years upon years of data. With the never-ending flow of data, the value of that historical information is priceless. Fortunately, storing it doesn’t have to derail budgets. Crawford has found CTUIT to be an affordable option because My Fit Foods is not investing in the infrastructure to house the data. “The monthly fee allows us to take that monthly charge and extrapolate it over a longer period of time rather than having to build our own back-end web services and write all the interfaces directly,” he explains. “That’s a huge benefit from a cost standpoint. The fact that we can get more granular, detailed data in real-time had an immediate impact on the operations of the stores. We can take actionable steps immediately rather than waiting a week or longer, so overcoming that cost was not very hard when you look at the data we get and the information we can pull out of it.”

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