Leveraging Mobility Data for Retail Market Planning & Site Selection

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Retailers are increasingly using mobility data to support site selection and market planning decisions. 

Mobility data helps brands:

  • Develop demographic profiles on the customers that visit their locations
  • View the other places where their customers shop
  • Compare visitation trends across regions

This information – combined with migration patterns and competitive analysis – unlocks valuable insights to power data-driven decision-making. 

To showcase how mobility data can be used in retail expansion decisions, our team analyzed Unacast’s data on the luxury retailer Neiman Marcus to develop a data-driven methodology and set of findings on where the brand could expand next.

Neiman Marcus’ 36 US Locations

One location in Hawaii not shown on map

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The High-Level Market Planning Process

Good site selection is the result of a methodical market planning process. 

Think of it as shrinking the search window so that in the end, a team is focusing its time in the places with the highest potential for the business.

This process starts by identifying which new markets (city, county, metro) have potential based on what the business needs in place to be successful like market size, migration patterns, or area demographics.

From there, a brand can zoom in on the narrowed set of markets. Which zip codes or neighborhoods show strong product-market fit for the business? 

This process results in retailers being highly strategic and targeted in where they’re searching. 

It moves a brand from a search region (i.e., “We want to grow in the Southeast”) to specific markets to specific areas in those markets.

This process relies on:

  • Having a deep understanding of the current customers – Who do we search for?
  • Analyzing migration patterns and area demographics – Where are they living or moving?
  • Finding areas in a market with complementary brands or area characteristics – Where can the retailer reach these target customers?

We’ll take you through each of these steps to show where mobility data supports better decision-making.

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Knowing the Customer: Building Robust Customer & Location Profiles

To find the best next location, it’s valuable to first understand the current customers and create data-rich profiles.

Unacast’s platform provides extensive site-level information to learn more about the customers that visit a location, including:

  • How many visitors are there and what are the demographics of the visitors? 
  • How far do they travel from home or work to arrive?
  • Where are the other places that they spend time?

For Neiman Marcus, our demographic analysis of the visitors to their locations reveals that their typical customers are:

  • High-income earners
  • Well-educated and living in the suburbs
  • Young and middle age working professionals

On average, 41% of Neiman Marcus shoppers have a household income above $100,000 (compared to 33% of the US) and their most common customer segments are various types of upper income.

Example Metrics of Shoppers at a Neiman Marcus Location

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Leveraging dynamic trade area data, we can understand not just the demographic profile of customers but also where they come from. How far will someone travel from home or work to visit a location?

Neiman Marcus customers travel ~20 miles from their home to visit a store, a fairly wide catchment area and a number that’s indicative of a more suburban store footprint.

Another important data point to know in site selection is understanding the other places where a customer spends time. This information helps determine the brands around which to locate in order to maximize customer reach and convenience.

An analysis of Unacast’s cross-visitation data shows that customers that visit Neiman Marcus locations also shop at other high-end luxury brands.

Brands with high cross visitation include Wolford, Giorgio Armani, luxury retailer Chloé, and Prada, suggesting proximity to other higher-end retailers is an important aspect of site selection for the brand.

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Collectively, this data provides a holistic profile of the customer base and becomes a blueprint for building a data-driven site selection framework.

Building the Data-Driven Site Selection Framework

A data-driven site selection framework connects knowledge about the traits of the target customer to the areas where these people can be found.

Among other factors like commercial property availability and cost, a good retail site and area is:

  1. Convenient to where the target customer lives, works, or spends time
  2. In a region that has positive migration patterns or flow of people
  3. In an area with the right surrounding complementary brands or characteristics
  4. In an area with sustainable levels of competition

Using the findings for Neiman Marcus, we know that we want to search for a target customer that is working age, high income, and located within a range of ~10-20 miles from a search area.

By analyzing the cross-visitation data, we also know that a location should be in an upscale shopping center or near other brands with high income shoppers, preferably in a suburban area to be close to the target customer and to find a storefront with high square footage.

Finally, we’ll be looking for areas that have positive migration patterns overall and from existing trade areas.

Starting the Search: Analyzing & Understanding Migration Patterns

Analyzing migration patterns to look for markets of sufficient size and with positive in-migration is a quick way to focus on places with near-term momentum.

Mobility data builds on this by providing information not just on where people are going, but also where they came from.

This is particularly beneficial for brands with a nationwide footprint. If certain regions where a brand operates are experiencing a declining population, mobility data can show which areas are benefiting from the out-migration. 

These households that move from an existing trade area are more likely to already be familiar with the brand along with bringing their existing preferences to the new market.

Leveraging Unacast’s migration data, we looked at migration patterns from three metro areas where Neiman Marcus operates today that are experiencing high out-migration: Los Angeles, San Francisco, and San Diego.

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We looked at where these households were moving and found five large metros where the brand doesn’t operate today: Seattle, Portland, OR, Minneapolis, Nashville, and Salt Lake City.

In 2020, Neiman Marcus closed a store in Seattle due to the challenges of COVID. This market will be removed from consideration.

In the remaining markets, an analysis of location information from Unacast’s Venues data showed that Nashville had the lowest level of direct competition (Saks Fifth Avenue and Nordstrom).

We’ve focused on that market for further research.

Finding High-Potential Areas in a Market

Zooming in on the Nashville metro, we analyzed migration patterns at the more granular level of zip code.

We honed in on an area with high in-migration, zip code 37064, which is south of downtown Nashville in Franklin. This suburban area had net population growth of ~6.5% in just 3 years.

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Drilling down a level further in the Unacast Insights platform, we saw that the median income in this zip code is $110,000 and the median age 41 years old. 

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The area and surrounding zip codes have high demographic alignment with the target customer we identified earlier, all within the 10–20-mile trade area that a customer will travel.

We found that the nearest direct competitor, Nordstrom, is about 15-20 miles away from the zip code. One department chain competitor in a similar market segment, Dillard’s, operates nearby.

Vetting the Area for Complementary & Competitive Brands

With demographics, migration, and competition checked off, the last step to validate this as a high-potential area is feasibility. 

We want to confirm that there are places where the brand could plausibly locate. An area that’s 100% residential or with limited complementary brands may have strong demographic fit but low area potential. 

Considering the areas in and near this zip code, we found a stretch of space to the northeast of the zip code boundary with high potential. 

There’s a mall with complementary tenants like Apple, where Unacast data shows 35% of shoppers have income above $100,000. The mall has 4 department store tenants, only one of which is in a similar segment (Dillard’s). Should one of the four leave, this may open an available space.

Another retailer nearby is Whole Foods, where 46% of shoppers at that location have income above $100,000.

Just to the south of the mall is a second retail hub with a Bed Bath & Beyond, which is soon closing all locations and may present an available space depending on the square footage and other factors specific to a Neiman Marcus location.

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These are all indicators that the area is feasible and could have strong product-market fit for Neiman Marcus due to:

  • Bordering zip codes in the trade area are high-income, suburban, and growing
  • Retailers with high-income shoppers operate nearby as do retailers that lease large spaces
  • There appears to be limited direct competition in the area

From here, a brand can layer in other factors specific to their expansion goals knowing that the area has the right initial characteristics in place for a new location to succeed.

Final Thoughts

Data-driven site selection is about focusing a team’s time and attention in the areas that have the highest potential for the business. 

There are factors beyond the data in every expansion decision but knowing where to look reduces uncertainty and increases efficiency.

Mobility data enhances this process by helping brands develop clear and actionable customer profiles, surfacing migration patterns near real-time and at scale, and identifying true trade areas and cross-visitation.

This information can then be used to develop highly targeted searches to find the areas that have strong demographic fit, area alignment, and alignment with the expansion goals specific to the business. 

By bringing these pieces of information together and into one platform, brands can take a more data-driven approach to identifying high-potential growth opportunities for their business.

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