Using Location Intelligence to Attract Retail to Underserved Areas (cont.)

This PSYTE population profile chart shows a sample from one trade area of some of the 72 lifestyle clusters that could show up.

One preferred method of defining a trade area is to use drive times. A drive-time trade area is more reflective of consumer thought patterns because people typically decide where to shop based upon how long it will take them to get there. However, be mindful that neighborhood or convenience- based retailers will have much smaller drive-time trade areas than larger stores such as Target or Home Depot.

The drawback to drive-time trade areas is that they do not take into account the location of competition, and physical or psychological barriers. The best option is a customdefined “true” trade area. A true trade area may not be symmetrical, but it accurately reflects the drawing power of the community. This trade area is delimited by competing retail nodes and physical barriers such as rivers. The dividing line between a low-income and high-income area also is merged into the trade area. Once the trade area is developed, basic demographic data are gathered to describe whom the retailer will serve. More sophisticated information also must be incorporated to show the retailer that the community has an understanding of its customer base and why the retailer would achieve success at this location.

Getting the demographic details

Basic demographic data on a municipality can be obtained from a variety of sources, but it is important to be sure that the data provide an accurate demographic analysis. For example, U.S. Census data are already seven years old and will not be viewed as very relevant by retailers.

The specific demographic factors retailers seek include:

• Population and households (new developments should be included in the growth projections)
• Average and median household incomes
• Per capita income
• Daytime work population
• Education levels (the trend in younger, more educated people locating to cities can improve this data)
• Median age
• Percent homeowners
• Percent families with children

Along with basic demographics, lifestyle (psychographic) data are measured. Lifestyle data delve into individuals’ shopping preferences and examine spending styles. These spending figure data are critical information for a retail operator considering a new site.When trying to attract retail to an area, the municipality can use this information to help determine which retail operators are the best fit for their community, thereby spending time and resources on those retailers who have the best chance of being attracted. A trade area may have 200,000 people, but if only 14,000 are the retailer’s core customers, the retailer may not be successful.

Conversely, a trade area could have 25,000 people, and if 20,000 are the retailer’s core customer, it is more likely that retailer will choose that location.

Lifestyle segmentation enables the municipality to determine the pre-defined lifestyle “clusters” in the trade area. A user can access information regarding the characteristics of each cluster to determine where the population is shopping and what they are buying, which in turn defines consumer habits and shopping/spending preferences.

Finding the perfect match

The goal behind all this analysis is to understand how retailers can be attracted to a particular community. Some of these matches are obvious, while others may take a little more research. For example, lifestyle clusters that are focused on more mature populations are more likely to need retailers such as drug stores, conventional grocers, medical supply stores, arts and craft stores and moderately priced apparel.

Likewise, in areas that reflect upper income groups, residents will be more likely to shop at Restoration Hardware, Pottery Barn, Ann Taylor Loft and Chico’s. They may also shop at stores such as Tuesday Morning while avoiding what they would consider low-end stores, including many “dollar” store franchises.

In addition to the demographic and lifestyle characteristics, the data reflect the buying power of the trade areas. Data on expenditure potential can be obtained through most demographic data vendors and also from the U.S. Census of Retail Trade, which is updated every five years and based upon actual sales. Data gathered on consumer spending in the area can be used to illustrate the potential success for a retailer, particularly in conjunction with information on known competitive locations.

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