Real Estate

How Location Data Figures into Your Business Expansion Strategy

By Thomas Walle, CEO and co-founder, Unacast

As we start to move into the post-COVID-19 world (even as cases continue to proliferate,) businesses have re-opened and much has returned to normalcy, but the landscape has dramatically changed. People’s patterns have changed. We’ve seen a significant number of people moving out of California, for instance, while cities in Arizona and Texas (among others) have gained residents.

How people shop has changed, too: eCommerce saw a 14.2% increase in 2021 from the prior year, according to data from the U.S. Department of Commerce Retail Indicator Division. And accordingly, with these changes – and others, such as those in the competitive landscape – companies can’t retreat back to their pre-pandemic methodologies for site location and other business expansion.

These decisions need to be made looking at the new baseline in today’s not-so-normal world. Navigating through these changes can be made much easier with the use of location intelligence, and specifically, human mobility data, which focuses in on how people move around.

Changing demographics and new locations

Population hubs were already changing long before the pandemic, but the last two years have ushered in new ways of movement. For instance, there’s a general trend of people moving from densely populated urban areas to less crowded neighborhoods in a different county or state. Texas is now home to 9 of the 15 fastest-growing cities in the U.S. based on percentage of population growth between 2010 and 2020. And California saw a 12% increase in people moving out of the Golden State compared to pre-March 2020.

In addition to people moving to new locations, we’ve also seen changes in foot traffic around certain hubs. For instance, with more people now working remotely at least part of the time, many downtown centers are seeing less traffic than they used to on weekdays. But this is all highly variable by region. Our research found that foot traffic in some parts of downtown Cincinnati, for one example, had decreased by 59% since 2019, while foot traffic in downtown Buffalo has actually grown to 84% of 2019 levels.

These are the kinds of details that business leaders need to be able to look at when determining, say, where to open the next location of their store. 

Using location intelligence to gather information

Being able to gather information about how many people are visiting certain locations, what time they’re visiting and where they’re going next can be invaluable when it comes to making decisions. Going a step further, what if you could also collect information about demographic profiles: Are these visitors male? Female? What’s their income? Are they just there for work? Do they live locally, or are their tourists?

It’s really about being able to collect information about not just the volume of traffic but the demographics – and then comparing this to the data from pre-COVID times. A company’s real estate group, for instance, would want to focus on a specific set of information such as:

  • Areas with a growing population,
  • Benchmarking competitors’ foot traffic and visitation and identifying unknown competitors, and
  • Areas that have opportunistic gaps in the local marketplace.

Acquiring migration patterns datasets would enable you to measure census tract growth by month for a given area. So, for example, if you were looking to expand business in Michigan, you would search for locations where the population is increasing. A second indicator – the income level of new arrivals – would also tell you this could be an ideal area for a new store. But first, you need to know who your competitors are in the area and how they’re doing.

By using retail data analytics and mobile location data, you can look at the peak visitation and foot traffic patterns around competitors’ stores. This will help you make informed decisions about where to locate and how to position your store.

Now, to identify gaps in the market, you would look at the distance traveled by visitors to competing stores. Perhaps you notice that most of the visitors come from within seven miles of the competitor’s store, but about a third travel from farther than seven miles away. It might make the most sense, then, to find a retail location closer to those customers who come from farther away.

This is the kind of information that location data encompasses – and while this isn’t the only set of factors in determining site selection – it can and should be a key component in making informed business decisions such as these. It’s an important piece of the puzzle. In fact, one of the most significant factors in the success of a new retail location is a potential customer’s ability to locate it on a map. Location data offers valuable insights into the best areas a shop owner could open a business.

Using location data for informed decisions

Location data offers many benefits, but it hasn’t always been so accessible – and historically, the accuracy hasn’t always been viable. And on top of that, working with these types of datasets traditionally required having an internal data team, something that not all businesses had and many still don’t.

The good news is that today, this type of data is becoming more democratized and accessible, so you don’t necessarily need an internal data science team. In addition, enterprises have accelerated their digital transformation; large organizations’ ability to work with data is now rapidly improving.

Much more than real estate

Retail site selection isn't just about retail real estate transactions. There's a whole world of retail store analytics packed into the site selection decision-making process. Key to attracting potential customers to any new location is determining its potential for store foot traffic. In urban trade areas, where both foot traffic from potential consumers and retail competitor density tend to be high, using location intelligence to inform the site selection process is key.

These decisions need to be made in light of post-COVID patterns. Human movement patterns have changed, so having the most up-to-date data is essential. Creating stores in new locations involves exploring population indicators, migration and income patterns, and neighborhood recovery indicators. Best practices recommend using location data to assess different potential store locations to deftly and accurately inform the process of retail site selection.

About the author

Thomas Walle is the CEO and cofounder of Unacast, a human mobility data company committed to understanding how people move around on the planet.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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