Capstone Identifies Future Cornerstores: Part 1

By Pan Demetrakakes, Retail Leader

Stocking the store must automate, while customer outreach needs the personal touch—enhanced by data.

Those were the takeaways from the two winning presentations out of the six heard at the Capstone conference of the Food Industry Management Program on April 6 at the University of Southern California.

The Food Industry Management (FIM) Program is a continuing education program for food industry professionals, in grocery and consumable goods companies. Now in its 48th year, FIM is part of USC’s Marshall School of Business and is sponsored by the Western Association of Food Chains.

The program is a sort of continuing education boot camp for mid-level managers, who may or may not have college degrees. The highlight is a series of presentations by teams of six FIM students. The teams are tasked with identifying a trend or area of interest to the food/grocery industry and putting together a presentation, describing its relevance and offering advice, to an audience of executives. The audience then votes for the two winning teams.

Here is the first of two summaries of the winning presentations:

Age of You

The potential for data-driven shopper personalization was the topic of Team Age of You. The presentation was led off by Billy Brink, operations coordinator for the Ralphs Grocery banner of Kroger, who raised the possibility of shoppers being greeted by employees who know all about their preferences and past purchases.

Brink talked about his mother, whose own mother and grandchildren love drinkable yogurt so much that she shops only in stores where it’s on sale. He talked about his neighbor, a caterer whose signature smoothies require massive amounts of avocados. A store that targeted them with consistent deals on those items could lock up their patronage indefinitely.

“You can do this by combining data with artificial intelligence,” Brink said.

This kind of personalization is especially important for younger shoppers, said Lizette Gonzalez, marketing project manager for Northgate Gonzalez Markets, a privately held chain in Southern California.

“Generation Z is the new ‘Me’ generation, or ‘Age of You’ generation, and they will expect a personalized predictive shopping experience,” Gonzalez said. “You need to be not only relevant, but super-relevant.”

This can be done through predictive data paired with artificial intelligence, she said. Pure online retailers like Amazon mostly have the technological advantage in this regard, but they don’t have the potential for personal outreach of brick-and-mortar retailers.

“How do we out-Amazon Amazon?” Gonzalez said. “We do this by giving consumers a reason to shop at our stores. Amazon may know what our customers want online, but they do not truly know our customers....You need to start using your data in a way that will allow you to be super-valuable and provide a personalized, predictive shopping experience for your customers, and that will allow you to cement that relationship for a lifetime.”

Retailers already practice customer segmentation, but modern information technology allows that segmentation to be so intensified that each individual shopper becomes his or her own “segment,” said Hugo Quimbaila, a regional sales manager with Bimbo Bakeries USA.

Quimbaila cited Sephora, the French cosmetics retailer, as an example of expert shopper segmentation. He said that Sephora’s emails to customers have a 91 percent open rate, because the customers expect the offers in those emails to be “super-relevant.”

“Can we imagine if in our industry, we were able to send communications to our customers, and about 91 percent of them anxiously waited for those communications?” he said. “At that point, in our 30,000-customer store, we have divided them into 30,000 segments.”

Lonny Reiber, a district HR coordinator for the Fred Meyer banner of Kroger, said that sales data can be enhanced by other data available through social media.

“When we can take our structured transactional data, combine it with social data—from Facebook, Twitter, wearable sensors—we will create super-relevant data, with that convergence of who and when,” Reiber said.

Once data are amassed, they must be properly analyzed. Zac Curhan, a product development manager at Niagara Bottling, described the analytical software that can do this as “a black box” with algorithms that can learn with successive data inputs. Curhan acknowledged that specifying such software is a daunting task that is well beyond the technical capabilities of most retailers.

“Our industry is great at many things, but we are behind in technology,” Curhan said. “We should stick with our core competencies and partner with firms who can help us bring predictive technology in- house.” He suggested pairing with an established data analytics firm or using Kaggle, an online platform that matches challenges in analytics with data scientists.