Retail GIS—Localization, Not Just Location

The importance of knowing your neighbor

Dorothy, this isn’t Kansas anymore. It could be Anytown, USA. On my last trip to Kansas, it wasn’t the wheat fields or flatness that amazed me but the repetitive retail landscape. It seemed that every town was a clone of the one I had just left—the same restaurant chains, grocers, drugstores, and general merchants. Was it an unholy alliance? Had real estate developers, government, and retailers reached perfect agreement on what every town needed and limited the choice to a small menu of options? However, the more I looked, the more I found exceptions. The harder I tried to quantify the way towns were similar to each other, the more I noticed the differences and came away knowing that local flavors dominate.

Doing business locally is the new kind of normal. After years of building out networks almost without limit, the current recession changed everything. Retailers that bucked the trend did so because they have what their customers want: stores in the right markets, the right products for their catchment, and enough sales opportunity to overcome competition and changing consumer tastes. Location and geography-based analysis have helped companies shift focus from opening stores to improving store revenue and creating better promotions. Coupons have become cool again. We’re not just clipping them from the local paper. We’re willing to get them online and via our phones because we benefit from letting retailers integrate our online habits with our in-store purchases.

Retailers get a lot from this exchange because everything is local. The lifeblood of a store is return customers. With detailed, local knowledge, retailers can go beyond segmentation and customer profiles to individual characteristics, localized assortment management, and product-level stratification. Loyalty and CRM data comes alive, so companies can spot trends and respond, reduce markdown risks, and improve the balance sheet.

Like Dorothy, I know there’s a journey that we need to take to gain courage, a heart, or knowledge. Are we ready for the challenges on the yellow brick road? I don’t know, but GIS sure looks like a good weapon against the miseries of the Wicked Witch of the Great Recession.

Is being local really helping retailers succeed in delivering what customers want?

Helen Thompson

About Helen Thompson

Helen Thompson is responsible for global marketing strategies in the commercial business development team at Esri. She believes that we are entering a phase of business platforms and geographic understanding supported by Location as a Service (LaaS). This will change the way we think about IoT, Driver-less Cars, Wearables, Big Data and a whole lot more.
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  1. Jim Stone says:

    How often will a customer visit your store if it is 2 minutes away from the customer’s home versus 10 minutes away? If there are less affluent people living near the store and more affluent people living further from the store, how much business will you capture from each?

    The increased focus on localization has cast a new light on a well-established concept in site analysis: the primary trade area. A primary trade area is generally defined as the physical boundary that represents some significant proportion of customers who will frequent a store, usually between 60 and 80 percent. Many techniques have been devised to estimate the size of a primary trade area for a proposed store including standard rings, drive times, and probability-based measures using advanced techniques such as spatial interaction models.

    However, there are two major challenges presented by the use of a single geographic boundary to define a store trade area:
    (1) What does the area outside the trade area look like, and does it really represent the remaining sales beyond 60–80 percent?
    (2) Does the probability of patronage change uniformly from the store to the edge of the trade area for all stores?

    After 15 years of analyzing actual customer data for retailers, restaurants, and service companies, we have found that it is almost impossible to consistently predict sales for a store using a single boundary as the measure of a primary trade area. As the distance from a store increases, the distributions of sales for real-world stores are too diverse given the varying quantity and demographic profile of customers.

    The state of the art in spatial analysis is now moving into new areas with techniques such as geographically weighted regression, geostatistical analysis, and other models based on continuous measurement of data across different distances from a store. Primary trade areas may be useful for visualizing existing customer data, but accurately modeling potential customers will require better techniques as chain operators seek growth and profitability from fewer, better located stores.

  2. Lori Schafer says:

    If the Wizard of Oz were written today, Dorothy would probably have her own smartphone. Based on her current location, she’d have a GPS-based app showing her how to navigate the yellow brick road back to Kansas. Along the way, she and her friends could use the smartphone to search for the nearest retailer who carried a heart for the tin man, a brain for the scarecrow, and courage for the lion. She’d also use the device to do comparative price checks; research which retailers were offering incentives; read reviews from others who purchased those same products; and, via social media, ask opinions of her family back in Kansas. She may even use foursquare to become the Mayor of Oz!

    In today’s world, Dorothy would be a typical tech-savvy consumer. Customers are now in charge, and successful retailers must not only better understand local customer preferences and differentiate their stores from competition but also engage with customers on their own terms. Today, it’s all about bringing your store to the customer, not expecting the customer to find your store.

    Savvy retailers understand the criticality of becoming more local. They are focusing on understanding neighborhood demographics and tying customer loyalty, purchase, and location data together to tailor assortment, style, size, and even colors to local customer demand. Responding to local customer needs is an essential strategy for most retailers. It’s proven to not only enhance customer satisfaction but also drive incremental sales and margin.

    Consider that one of Macy’s core strategic priorities is “differentiating merchandise assortments and tailoring them to local tastes.” It’s My Macy’s initiative is all about making the company’s merchandise specific to customers’ needs in every store, in every local market. Best Buy provides consumers with access to a store-specific Web page for each of its locations. Its mobile application includes a store locator and will soon provide the ability to search for a particular item and, based on a customer’s current geographic location, show in-stock positions for nearby stores. Both Best Buy and Macy’s are testing a mobile-based customer loyalty program that detects when the customer is in the store, then presents relevant incentives based on that customer’s specific profile and location.

    Over the past several years, retailers have begun implementing analytic software to help them tailor marketing to the local consumer; optimize the price, quantity, and assortment mix based on customers shopping that particular store, and improve operational performance by location. Software applications such as market-basket analysis; demand forecasting, campaign, assortment, size, price, and promotion optimization have become mainstream in assisting retailers in tailoring merchandising and marketing to the local consumer.

    To date, GIS has been used mainly by retailers’ real-estate departments for location planning. Yet GIS should also be a key tool used by retailers’ merchandising, marketing, and operations departments in tailoring assortments, services, and incentives to local demand. GIS can easily be integrated into these analytic software solutions to provide a more precise view of local market conditions. Consider how much more insight a retailer could get by seeing a computerized map showing the precise location of all stores, complete with a detailed view of competitors’ relative locations as well as complementary retailers and other services that could draw more traffic. Demographic, store performance, assortment, pricing, and customer data associated with each location is only a click away.

    Retailers and retail solution providers need to fully embrace the capabilities that GIS can provide them. After all, most consumers are already using GIS on their mobile device to find what they want. Very soon, consumers will be able to type the name of a product into their smartphone and instantly see the list of local retailers who have that item in stock, associated price and incentives, and directions and drive time. With the rapid surge in GIS-enabled mobility giving today’s customers all the information they need in the palm of their hand, retailers need to not only understand who and where their customers are but also how to optimize their stores’ merchandise, services, and promotional offerings for that customer. There is no better way to know your customers, assess the marketplace, and improve your business than by incorporating GIS and location data into your business analysis.