What good is customer data if it’s difficult to analyze?

Quick-service restaurants have long collected customer data, but as the pandemic has required brands to track customer behavior and feedback more closely than ever before, a new problem has arisen: How can a brand aggregate the data from various channels and quickly turn it into actionable insights?

“Large brands collecting data at an enterprise scale have so many different sources of that data that it can be hard to parse,” says Duane Lyons, practice lead at Wavicle Data Solutions. “When you have tens of millions of feedback data points, being able to analyze at that scale and pull out the golden nuggets is a real challenge, but it must be done.”

For example, one of the largest quick-service brands in the world had data flowing in from places like social media channels and customer surveys—not to mention less traditional channels such as online chat conversations, and transcripts of conversations between customers and call centers. The fact that the brand had collected data totaling over 50 million textual comments was very exciting, but none of that was very useful if it took tens of thousands of hours to compile into easily digestible information.

So the brand teamed up with Wavicle Data Solutions, a data analytics consulting company, to build a customized platform—leveraging Wavicle’s ActiveInsights as a starting point, a cloud-based platform that operates within the Amazon Web Services (AWS) public cloud—that would seamlessly integrate raw data from those multiple sources and break it into insights that could be used to help better understand customers and what they were looking for.

For example, let’s say a customer left a comment online that said, “I ordered a burger, it was delicious, but the fries were cold.” Lyons says that Wavicle’s solution would break that down into three “sub-comments”: first, a neutral declaration that the customer ordered a burger. Second, a positive point that the customer enjoyed the burger, and third, a negative note that the fries were cold. Since the restaurant brand’s footprint includes over 10,000 units in the U.S., the platform would be able to reference other data in order to help understand if this was a one-off problem, a store-based problem, or something that was occurring across an entire region of the brand’s footprint.

With Wavicle’s solution, large restaurant brands could translate the feedback into visualizations for franchisees and restaurant owners that highlight patterns of positive or negative feedback so that the insights can then be put into action at the restaurant level. For example, if there’s consistent negative feedback about cold fries at a particular restaurant, adjustments can be made in the french fry preparation process. And if positive feedback rolls in, things like bonuses or awards can be handed out accordingly.

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One pandemic-specific example of data in action was that the top brand immediately noticed in spring 2020 that their customers felt much safer if drive-thru crews were wearing proper PPE.

“That helped re-engage stricter protocols at the restaurant level and create a better experience,” says the top brand’s director focused on customer insight. This was just one of the many things the quick-serve brand learned after implementing ActiveInsights, and it won’t be the last, because the solution is still hard at work analyzing the data as it pours in in real time.

“We’re not done with the work we are doing,” the brand’s director says, “But we’re proving that it works.”

To find out more about the custom platforms Wavicle Data Solutions can build for your brand, visit the company’s website.

By Charlie Pogacar

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