Editorial
The National Retail Federation reported only 47 percent of retailers are using insights from customer analytics to drive operational decisions. And to think ... the retail industry is considered one of the more sophisticated across sectors for analytics use.
And that generally is the case — when it comes to leveraging customer analytics for marketing promotions, loyalty programs and merchandising decisions.
"Many still do not leverage customer analytics for other operational decisions, often because of a lack of fully integrated customer data as well as integration into other operational data and sometimes simply because of the lack of knowledge of how to leverage operationally beyond those areas mentioned," explained Dave Nash, director in the Customer Experience practice at consultancy West Monroe Partners.
Not So Fast
Eleanor McDonnell Feit, assistant professor of marketing at Drexel University and a fellow of the Wharton Customer Analytics Initiative, disagreed.
"I’d peg the number of retailers who’ve made investments in improving their customer data assets far higher than 47 percent," she said.
Many retailers have made "great investments in collecting and organizing customer data," she goes on. She concedes that what is less common is a company using that data to drive decisions across all functions.
"That might only be happening at a couple of the most data-driven retailers," she said.
The big customer analytics wins that Feit has witnessed have occurred within "largely isolated pockets" within a company, places where the data come together with "smart analysis" and execution.
An example might be a marketing professional who manages catalog and email campaigns, and to that end wields data, A/B testing, segmentation and ROI tracking. But Feit does not see trends for where these "pockets of analytics" appear across companies.
"The successes seem happen when a smart leader spots an opportunity and has the discipline to execute on it," she said.
Customized Solutions
Whatever number you use to quantify the retailers applying customer analytics for operational purposes, they are applying customer analytics to as much of their business as possible.
Feit doesn't point to any one retailer as an example. Instead, she mentions a cutting-edge practice: recommendation engine.
Recommendation engines are automated tools that help customers find what they want, even when they don't know what that is. They can become powerful engines of growth.
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"This isn’t something you can just buy off-the-shelf from an analytics vendor. Great recommendation engines are customized to your business and your customers, encapsulating what makes you special as a retailer," Feit said.
Who Stands Out?
Nash points to a specific company as a great example of customer analytics use: Kohl's.
"They are using customer analytics applied to mobile technology to personalize and enhance the in-store customer experience," he said.
For instance, in-store beacon messaging, driven by customer analytics, provides shoppers with targeted promotions as they shop. The department store applies analytics throughout customers' "buying journey," from social media promotion through loyalty programs, while also segmenting its customer base and personalizing services and messaging.
The company also applies the concept of customer centricity. They double-down efforts on their most loyal, most profitable customer groups.
It's safe to expect more retailers to follow in Kohl's and its peers’ footsteps.
"I’m hopeful that as this new generation of data-driven leaders move up within retail organizations, we will see companies start to take a data-driven approach across more aspects of the business," said Feit.
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