How to use customer data to plan store layouts

By Graeme McVie, LoyaltyOne

You've already invested in customer data collection, data management and formulating insights. Now it's time to start exploring the myriad ways to apply that valuable data to boost the bottom line. Tweaking store layout is one way to help earn maximum return on your investment.

Historically, retailers have relied on a variety of methods, including customer surveys, geo-demographic information, sales dollars and sales velocity when it comes to determining the most efficient store layout. But those methods often fall short of their full potential. By augmenting those traditional approaches with customer-specific basket data, retailers can get a deeper look at the behavior of priority customers, and adapt store layout accordingly.

Customer information helps provide a better understanding of the lifestyles and life stages of a particular store's shoppers. It can be used to identify affinities between items or any types of patterns. Data can then be applied to a number of both promotional and physical re-merchandising scenarios that target a company's best customers.

Adjusting shelf layout

An East Coast grocer noticed that sales of adult cereal were lower in some stores than others. When they looked at their pricing, promotion, assortment and planogram (visual product-placement planning) decisions, they couldn't spot any clear pattern. However, when they analyzed customer data, they found an explanation — they were under-penetrated with their senior customer segment, but only in certain stores.


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Organization of the cereal aisle with kids' cereal on the bottom shelves, family cereal in the middle and adult cereal on top, created the greatest under-penetration. But when the aisles were organized into eight-foot sections, one each for kids, family and adult cereal, the sales were corrected. Making it easy for the senior customer segment to reach the adult cereal was a key driver to penetration.

The grocer reset the shelves, but only in stores with a high number of shoppers in the senior customer segment, thereby minimizing disruption and cost associated with the reset. As a result, for the senior customer segment, cereal penetration increased, basket sizes increased, sales increased and profits increased.

Cross-merchandising

An auto parts chain discovered two interesting insights when analyzing customer data: First, in certain stores, sales of motor oil were lower among commercial customers than among consumers. But replacement-part sales were in tune with expectations for both groups.

Further analysis on oil sales revealed that the retailer had a discount price plan for commercial customers that applied to parts but not to oil — even though more than half of motor oil sales were generated by commercial customers.

The second discovery was that in stores with more tenured sales associates, sales related to oil transactions were higher. Further study revealed that in these stores with tenured associates, salespeople would upsell customers by suggesting that they purchase oil pans, rags and filters along with motor oil.

The retailer implemented a two-part initiative that resulted in increased sales and revenues. First, it sent commercial customers direct-mail offers for oil discounts. Second, it conducted upsell training for less-tenured associates. Additionally, it approached store layout by establishing a special oil-change section that cross-merchandised oil, filters, pans and rags, and shoppers received a $5 discount if they purchased all four items. Signage that suggested cross-purchases was also employed. Every month, the section was updated to feature a different oil, filter, pan or rag supplier, creating partnership opportunities for the retailer

Changing physical layout

A grocery retailer identified some smaller sales that were occurring mid-week in late afternoon and early evening. It determined that these shopping trips were being made by two-income families with young children.These families wanted to prepare convenient, healthy meals during the week. The retailer subsequently altered the layout of certain suburban stores to create a convenient, one-stop shopping area at the front of the store for these high-value customers.

The new store sections included rotisserie chicken, fresh-prepared cut produce, bread, salad, a beverage cooler and a dessert section.The retailer installed these sections in store locations that over-indexed in the young-family customer segment.

In addition to physical store changes, the grocery increased staff and self-checkout counters during the important weeknight hours of 4:00 to 7:00 p.m. Then, a targeted direct-mail campaign was sent to the customer segment. It explained the change and offered a $5 off coupon for the next transaction conducted during this special three-hour period, if the customer purchased at least three items from the new section. Signage was also hung at the store entrance to draw shopper attention.

Today, retailers that have applied customer analytics to new areas are seeing tangible results in the form of higher transactions, bigger margins and larger orders in re-merchandised areas of select stores. By using customer data for more than staging targeted promotions, these retailers are reaping a true return on an investment that ultimately affects their bottom line.

Graeme McVie is General Manager of Business Development and Client Services at Precima, a LoyaltyOne Solution. (Photo by Anthony Albright.)

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