Development Magazine Summer 2019

Data Drives Decision-Making in Retail Real Estate

Retailers can use big data to maximize sales per square foot. They can also use it to determine the best location for a store, as well as the optimal building size. Getty Images

Analytics can guide store size and configuration, help save money and improve customer engagement.

In an era of disruption and evolution, all retailers are looking for ways to improve customer experiences to drive sales. They’re also looking for ways to cut costs. Maximizing real estate utilization through the use of data is a practical and measurable way of achieving both goals.

With the increased use of data, retailers are able to decrease the overall footprint of their stores, increase their sales floor by reducing back rooms and rely more heavily on an omni-channel sales strategy, which seamlessly allows customers to flow between online and in-store points of sale. Similarly, data helps retailers maximize their supply chains by allowing companies to become more efficient with warehousing and retail outlets. (See Commercial Real Estate and the Big-Data Deluge.)

Stores are Getting Smaller and More Cost-Effective

Retail analytics promise retailers the ability to maximize sales per square foot, and they’re using that information to come up with newer, smaller footprints for their store prototypes. Smaller footprints translate into lower rents and can reduce buildout costs, utility bills and common-area maintenance expenses.

In turn, many retailers are investing these savings into enhancing the consumer experience, as stores are no longer just spaces for displaying products. Special events don’t have to be limited to offerings such as cooking classes in kitchen supply stores. Apparel companies are hosting yoga classes, microbreweries are sponsoring community events and shoe stores are installing nail salons. Retailers who embrace innovation and dare to try new concepts will succeed in this ever-changing landscape.

Retailers have also been opening smaller prototype stores, especially in densely populated urban areas. In suburban regions, for example, Target builds large single-story units with big surface parking fields. However, in cities such as Boston, which can command high rent, Target’s urban prototype is a smaller multilevel store, which may or may not have adjacent structured parking.

Better use of space and improved in-store experiences for customers are not the only ways to use stores efficiently. Retailers are also using data to maximize their supply chains. As a result, they are able to shift more inventory storage from the backrooms of expensive shopping center space in densely populated areas to lower-cost warehouses in less densely populated areas. By decreasing the need for backroom storage, retailers are able to find additional rental, utility and common-area maintenance savings. However, the trade-off will be increased transportation costs because they’re shipping goods more frequently and in smaller batches.

IKEA, known for its mammoth stores, is opening smaller urban units called IKEA Planning Studios. Shoppers will be able to select and order IKEA products from the Planning Studio for home delivery. The first IKEA Planning Studio in the U.S. opened in April in Manhattan. Ahead of the opening, IKEA also opened a distribution center in the much lower-cost New York City borough of Staten Island.

Influencing Inventory Levels

Better alignment between sales and inventories translates into lower inventory costs, freeing up capital for other purposes. This is not a new concept; as early as the 1970s, Toyota implemented an early version of just-in-time inventory management. What has changed is the power and sophistication of the enabling technology. Real-time sales data by product and location combined with historical data sets provide more accuracy than ever for sales forecasts. Additionally, in-store “heat maps” and website analytics allow retailers to see — again, in real time — precisely how customers interact with products and displays. Collectively, these data sets can transform retail operations, if embraced fully.

From real-time inventory updates at the cash register to robots and sensors determining if a shelf needs restocking, inventory can now be very closely monitored. That granular-level information allows retailers to ship goods from lower-cost warehouses to their retail outlets quickly to meet the needs of customers. No longer is there a need to stock several of the same size, color and style of jeans in a store; when stocks run low, data in the retailer’s inventory system will alert a distribution center that it needs to ship those particular jeans overnight to a retail location.

At Old Navy, for example, if a customer cannot find what he or she wants, store associates can retrieve inventory information on handheld devices. At Famous Footwear, if a particular store location does not have the size, color or style a customer needs, associates can immediately order the shoes. The customer can choose to pick them up at the store or have them shipped to their home. Best Buy allows a customer to order a printer cartridge from the comfort of his or her couch and then pick it up at any store location.

These are a few examples of how retailers are using data to enhance the customer’s experience in a store, as well as seamlessly tie together the worlds of online and brick-and-mortar shopping to increase sales.

Data shapes real estate decisions. It is a driver in the supply chain, which correlates to how stores are stocked and ultimately how a store’s space is used. By innovating and adapting to the consumer through the use of data, retailers can continue to remain relevant.

 

Stacy Engles Wipfler is a partner with the Husch Blackwell law firm.

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