What Defines a Retail Trade Area Today?

Joe Francica 

Senior Director of Geospatial Strategy

August 16, 2021

What Defines a Retail Trade Area Today?

The trade area definition for retailers with physical, brick-and-mortar locations seems to have changed radically, even within the last few years. But has it really? “E-commerce was 11% of retail sales in 2019; in April of 2020 it peaked at a high of 18.4%; and finished 2020 with 14% of total sales,” according to Forbes. This means that most consumers are still shopping at their local grocery or department store to buy goods. Consequently, convenience and proximity to their favorite retail establishment are still key drivers in a choice of one retailer with similar products over that of a competing retailer.

However, what has changed is the amount and variety of data available to create and analyze trade areas.

The Traditional Way to Create a Trade Area Map

Traditionally, drive time, drive distance, as-the-crow-flies, ZIP code, census tract, designated market area or central business district were all methods of geographically defining a trade area. More demographic details such as income, age or ethnicity could further refine the geographic boundary data. These standard methods, though not entirely obsolete, are simply insufficient because of the plethora of additional location data that can be used to refine the limits of a trade area. For example, drive time and distance illustrate the impediments to consumer access by vehicles, while pedestrian mobility data is sometimes limited to specific retail centers such as malls or urban business districts where walking is the preferred method of travel. Individually, each method is useful; combined they add dimensionality.

So, it’s no longer viable to sustain revenue growth by simply using the standard measures of trade area definition, which in the past defaulted to a 3-mile radius from a store’s location. Today, a trade area map looks quite different and incorporates pedestrian and vehicular mobility patterns, traffic egress and ingress patterns, and may eventually show drone flight paths that consider terrain, building and tree height, and other obstacles so that deliveries adhere to service level agreements.

New Methods of Trade Area Analysis

When a consumer visits a physical retail location, there are both basic demographic characteristics as well as digital signals that identify the propensity to visit any single location. Both are required and ultimately define the geographic extent of the store’s trade area. Trade areas are defined using many variables as explained above, but more data precision and integrity are required to compete. While this data is readily available to all competitors, the savvy retailer must leverage all available data and spatial interaction models.

High-definition digital street network data provides enough information regarding roadway speed limits, historical traffic, and road classification (i.e., highway, residential, etc.) to predict travel times and distance with excellent accuracy. This data is being continually refined with up-to-date attribution, such as adding periods of high and low vehicular traffic, not just total average daily traffic (ADT). According to Street Fight Magazine, “the car is the ultimate mobile device. And it’s becoming local’s next battleground as more and more sensors are infused.” Most new vehicles include navigation with points of interest data that includes restaurant, fuel, and grocery store information. It’s fair to say that cars have become one more mobile device that presents consumers local information about where to find goods and services, but also supplies the digital signals for ingress and egress to shopping centers. When crunched using today’s geographic information systems, each data layer brings into clearer focus the factors affecting the extent of the trade area.

There are many other criteria as well. The location of credit card purchase transactions can be mapped to show the distance that consumers travel to a specific store location. Census demographics can be used to support transaction patterns. The use of mobility data, particularly footfall data and dwell time, can offer insights into the consumer’s profile, buying habits, and brand preferences. Mobility data offers more dynamic and current insights than historical demographic data. Consumer expenditures by item category and disposable income remain highly relevant to craft audience profiles and geodemographic analysis.

The Impacts of Technology on the Trade Area

If the COVID-19 pandemic has taught us anything, it’s that for many consumer staples, we still need to venture out to stores. However, because many retailers quickly offered options such as contactless BOPIS that provided an intermediate choice to venturing inside stores, brick-and-mortar establishments continued to thrive. Likewise, grocery chains and home improvement stores began providing lockers just inside entryways to pick up pre-ordered purchases. These conveniences delivered enhanced online and offline user experiences that leveraged the physical store location while simultaneously securing and often expanding trade areas for retailers that quickly pivoted to a hybrid model.

What else can we expect from technology and its impact on convenience and location? Will Retail-as-a-Service, Amazon’s cashier-less innovation, provide yet another “attractiveness” criteria that will draw consumers to a brick-and-mortar grocery store, like Whole Foods? Will drone delivery become just one option for improving customer experience and serve to build brand awareness with a localized community?

These are all factors that might potentially affect trade area development in the future.

Trade Area Usage in Retail Management

When the trade area is better defined, the marketing department can provide improved targeted messaging through direct mail, mobile ad targeting or loyalty app promotions. The improvement of the trade area can then be used for predicting total sales revenue. Better defining the geographic extent of the trade area supports merchandizing that better facilitates a hyper-local sensitivity to consumer needs and therefore more efficient overall management of the franchise.

With better definition of each store within a network of stores, the real estate and sales management teams can then look at store planning that identifies expansion opportunities or consider alternatives such as store closures or remodeling. A better understanding of the company’s network success allows management to understand total profitability, do human capital planning and take a long-range view of the best use of the company’s real estate investments. Optimizing the network depends on a fundamental understanding of location-based information that identifies each store location, its profitability, competitors and perhaps the reason for success or failure.

From a different perspective, retailers might look at a high performing store in the network to understand how and why that particular location generates higher revenue than others. Identifying the variables defining the success of that store makes it possible to use it as an analog model to replicate at underperforming stores. The entire network of stores would thus benefit from a more rigorous location analysis of each store’s trade area and likewise challenge the extent currently assigned to each.

The Macro View

Trade area analysis for a single store is certainly impacted by merchandizing, mobility, and demographic factors but there are also location variables at play. Sometimes geography gets in the way. Barriers, both physical and perceived, such as rivers, major highways, mountains, and railroads often limit access to retail. The mere perception of a barrier, such as a bridge over a river to another part of a city, will limit the trade area. In another example, competition may be a good thing, such as when you have retailers in proximity to each other. Each causes a positive attraction, such as when you see a Home Depot directly across the street from a Lowes. Both home improvement retailers benefit from the phenomenon of agglomeration. According to a recent article in Propmodo, “Agglomeration provides an economy of scale, savings costs by attracting suppliers, labor, and customers. It isn’t just retail hubs that grow and exploit economies of agglomeration, it’s what the entire basis of cities is based around. Concentration isn’t a result of economic activity, it’s the cause.”

The Science of Trade Area Analysis

The remarkable thing about the availability of location-based data today is that more precise thematic analysis and spatial statistics can be applied to trade area definition. Today’s geographic information systems are very capable of applying retail models, spatial regression, and map algebra that reduces multiple variables to those that are key to understanding which set defines the trade area most accurately. According to the Propmodo article, even game theory has become a tool integral to retail site selection strategy. So, defining a retail trade area has become the focus of a new era in data science that employs location data analytics.

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