Which retailer would be the most successful at a given location? Which types of businesses are the most complementary to each other? How far should a retail location be from a competitor? These questions have been asked as long as we have had shopping centers. The answers to these questions are incredibly complicated and require the expertise of experienced retail leasing brokers whose job it is to understand what works best for each location in their area.
The ability of a trained professional to understand an area’s retail landscape should not be overlooked. An experienced broker can have a great “feel” for a market and can be an invaluable tool to help landlords find the right retailer and retailers find the right locations. Intuition is invaluable, but it can always benefit from more data, particularly for a calculation as complicated as location strategy. Now brokers are using new analytical tools to help their clients succeed in finding and leasing the right retail space.
With the proliferation of smartphones that track our every movement, we have entered into a new era of understanding when it comes to shopping habits. Mobile app developers sell anonymized “mobility data” that show the demographics of the people who visit a certain retail location, everything from where they live and work to where else they visited on their trip to the store. This has helped landlords understand much more about the clientele of their properties and retailers know more about the habits of their customers. But even this is not always enough to crack the mystery of what makes the best tenant mix.
“Sometimes the best locations for a retailer are counterintuitive,” says Jay Panchal, co-founder of AlphaMap. The example he gives is big box hardware stores. Traditional location analysis is often based on the concept of competitive saturation. If there is a large enough area or population without a certain type of retailer close by, then it would make sense to put one there. But that doesn’t apply to large hardware stores. “You would not think that putting a Lowe’s close to a Home Depot, its direct competitor, would be a good idea but that is exactly what has worked for many locations around the country,” Panchal explained.
So in order to examine exactly what does work best when it comes to grouping businesses, Panchal and his team have created a new tool called TenantFinder that analyzes nearly every retail location in the US and looks at what brands and categories are the best match for a particular location based on multiple factors such as brand synergy, co-tenancy, and demographic match. This ability to understand the connections in customer behavior between brands completely changes how retailers and landlords are able to analyze site selection. “We started analyzing communities across multiple states and doing peer analysis to identify communities with specific concepts that we were thinking about pursuing,” said Chuck Branch, CEO of NextSite, a commercial development advisory firm. ”Then we identify communities without those concepts but with similar demographic profiles as places where that concept could be successful.” By using that technique and tools like TenantFinder, NextSite has been able to identify voids in the current retail landscape where new types of retail properties can be adapted or developed. 
The key to all of this advanced analysis is the algorithm and the data that it is used on. AlphaMap analyzes millions of locations nationwide and over 42,000 brands, which allows them to recommend best-fit brands for any location with a high level of confidence. “The retail environment is constantly going through a natural selection process, so by looking at what is working we can get a sense for how it is evolving,” Panchal said. “With the ability to analyze all this data en masse, we are now able to uncover patterns that have otherwise remained hidden, allowing us to figure out what brands are best suited for a particular space.”
As good as data science algorithms have become at recommendations, they are not always able to answer the important question: why? “The broker is still so important in this process because they are the one that has to tell the story to their client, to explain what the data means and why it can be trusted,” Panchal said. With TenantFinder, brokers are now able to make recommendations that are backed by data and outside of their normal knowledge base. “It is really hard to know about that new chain that might be killing it in a few towns over and is looking to expand into your region,” Panchal continued, “but that is exactly what brokers are tasked with knowing.”
Just as our retail habits are ever-evolving, so too are our cities. The property industry is tied to the city planners and economic developers that allow and incentivize new development. These agencies are also finding ways to incorporate technology into their decision-making process. “Communities understand the need to be proactive in their commercial development recruitment efforts to create jobs, increase tax revenues and support their existing businesses, and over the past few years, many are starting to manage the process internally,” said NextSite’s Branch. Eventually we might see cities push to not only change zoning to support economic development but actually request certain types of retail that they think would make the biggest impact.
There is no one perfect answer to the question “what retailer would do the best at this location?” The long-term success of a retail location depends on a wide variety of factors including execution, promotion, and product/market fit. But now, thanks to much more robust analytical capabilities of technologies like TenantFinder, the commercial real estate industry and city officials are able to back up their intuition with data and hopefully figure out the often fuzzy math behind site selection.
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