Before you open a new store or close or relocate an existing one, it’s crucial that you understand the opportunity it represents to your business. Trade area analysis gives you a framework to evaluate and compare real estate opportunities based on the characteristics of the surrounding area. Conducting this analysis effectively can make the difference between record profits and devastating losses.
For decades, trade area analysis has been the main way retailers evaluate and compare the demographics and key factors of each trade area in order to build site models and sales forecasts. More recently, GIS software like Tango Predictive Analytics has empowered businesses to accurately quantify these factors and develop reliable formulas, significantly reducing the margin of error.
Whether you’re looking for the best spot to put up a new drive through, evaluating the viability of a strip mall location, planning for the best route to expand your chain across the US, or reassessing your portfolio, trade area analysis helps you find trade areas that have a sufficient mass of customers to support a store. It’s the main ingredient in retail site selection.
Unfortunately, a lot of retailers have faulty assumptions about how this vital process works and what it entails. In this guide, we’ll cover everything you need to know about trade area analysis, including:
- Trade area analysis methodology
- Common trade area analysis mistakes
- The key to effective trade area analysis
Let’s start with how it works.
Trade area analysis methodology
Trade area analysis is more science than art. It’s about whittling down the extraneous characteristics of a trade area to isolate the factors that actually impact your potential sales. The more you can lean into quantifiable metrics and objective data, the more useful your analysis will be. Which is why it all starts with having the right tools.
Utilize GIS software
As with other market planning processes like white space analysis or void analysis, you can’t perform trade area analysis without geographic information systems (GIS) software designed for retail applications. Tools like Tango Predictive Analytics combine GIS mapping capabilities with machine learning and diverse datasets to help you visually examine sites and more accurately compare opportunities.
In trade area analysis, you’re working with a lot of information—performance data, demographic data, customer data, traffic data, census data, etc. GIS puts that data on the map, showing you where demographics are concentrated, how they move throughout the trade area, and where your stores are (or could be) in relation to commerce activity.
Once you have the location analytics capabilities you need, you can narrow down the area(s) you want to analyze.
Pick a potential location
Trade areas are based on specific locations. But when you’re planning for new stores, it can be a bit of a “chicken or the egg” situation, where you’ll likely use demographic and mobile data in software like Tango Predictive Analytics to see where there’s the most relevant commerce, then select a location, then analyze the trade area based on that location.
Your ability to access the opportunity a trade area presents completely depends on the features of the site you select. And if you’re evaluating your current portfolio, this whole process starts with your actual locations. Then, as you assess the surrounding trade area, you can consider whether another location is in a better position to maximize the opportunity.
While you likely have strong ideas about what criteria makes your top performing stores successful, machine learning will help you isolate the variables that actually matter through regression analysis, so you can build reliable site comparisons and then pick locations based on the true opportunity they represent.
Define the trade area
A trade area, also known as a catchment area, is the main geographic region where a given location’s business comes from. Typically, it accounts for about 60–75 percent of a store’s sales. Often, trade area analysis will involve defining a primary, secondary, and tertiary trade area, each with a different radius from the location.
There are no universal rules for what a trade area looks like (and you wouldn’t want that anyway, because every location and business is different), but the size of the trade area may be based on miles or drive time from the location, like this:
- Primary trade area: one mile radius around the location
- Secondary trade area: three mile radius around the location
- Tertiary trade area: five mile radius around the location
Or like this:
- Primary trade area: three minute drive from the location
- Secondary trade area: five minute drive from the location
- Tertiary trade area: seven minute drive from the location
Whatever your system, you’ll wind up with a defined shape surrounding the location. Keep in mind that the goal here is not necessarily to have a standardized shape or radius, but to map the area where the majority of sales come from. The chosen radii are just starting points. As you compare trade areas, they will likely have different shapes and sizes.
A trade area is a blunt instrument for assessing and comparing the quality and relevance of commerce activity near a location. Whatever its shape, a trade area’s value comes from aggregate data about the populations within the area, such as race, income, age, sex, education, marital status, and household size.
It’s also worth knowing whether the trade area is home based (where relevant traffic primarily comes from nearby residences), worker based (where relevant traffic primarily comes from the people who work at nearby businesses), or shopping based (where relevant traffic primarily comes from people shopping at nearby businesses). As you compare trade areas, this can have an impact on the general mindset of the average person who passes through the trade area.
Combine internal and external data sets
As you define each trade area, you’ll need to use a combination of your internal data and third-party datasets to see the overlap between your target customers and the trade area’s demographics.
Depending on your industry, you may have detailed customer or member data that identifies useful information about who your business currently serves and how much they spend with you. Or you may have purchased external data that provides these insights. The more accurate your data about your current customers is, the more reliable your trade area analysis will be. You’re evaluating the opportunity the area represents to your business, so it’s important that you can isolate the demographics that are most relevant to you.
Tango Predictive Analytics uses mobile movement data to help you understand what’s driving trade, so you don’t have to guess if foot traffic is coming from nearby workplaces, residents, or shoppers, and you can even see how much trade comes from transient populations that come and go.
You can also use geofencing to leverage this mobile data in other ways, isolating people who physically visit your stores (or even the location you’re considering!) and putting together aggregate data about them.
Without the right data, your trade area analysis may as well be “a guesstimate.” Leaving datasets out removes important variables from your calculations, which means your answers (how much opportunity each trade area represents) are always going to be wrong.
Want to learn more about the role your internal data plays in trade area analysis, site selection, and sales forecasting? Check out our free checklist, 7 Ways to Use Your Proprietary Data in Site Selection.
Identify meaningful characteristics of the trade area
Demographics and traffic flows aren’t the only things that influence the profitability of a location or potential of a trade area. Within each geographic area, you’ll find a different breakdown of competitors, complementary businesses and activities, nearby parking spaces, and obstructions.
Each of these factors affects how much of the trade area’s total opportunity a given location can tap into. And if you try to compare trade areas without taking characteristics like these into account, your analysis won’t be accurate. You can’t account for every feature—and you wouldn’t want to, because some have little to no impact—but overlooking significant site selection criteria can throw off your whole location strategy.
When you use Tango Predictive Analytics for trade area analysis, every location you consider receives a site score and trade area score based on how well it meets your specific criteria.
Calculate the opportunity
Trade area analysis goes hand-in-hand with sales forecasting. Your sales forecast is built on top of this process. But before you project the sales a specific site can expect to generate, you need to gauge the potential sales the trade area could yield.
This is where you’ll use what you know about your customers and the demographics within the trade area to find the maximum sales your business could expect with an ideal location and no competition. The more you know about your customers, the more accurate your calculations will be.
For example, as you compare trade areas, you may find that one has a higher concentration of demographics that typically spend more money with you, but another trade area has a greater volume of demographics that spend less. So which has the opportunity for greater sales? Using AI and machine learning, Tango Predictive Analytics can handle this for you, equipping you to analyze which trade areas best align with your goals and intent.
Calculating the opportunity is one of the most valuable components of trade area analysis, but it’s also the easiest to get wrong—and the stakes are high. The value of your calculations depends on the reliability of your datasets and your software’s ability to accurately utilize them. And that’s one of the main reasons why retailers and restaurants like Yum! Brands, Wendy’s, and Dunkin’ turn to Tango.
Now that we’ve gone through the basic methodology of trade area analysis, let’s discuss some of the main areas where retailers run into problems.
Common trade area analysis mistakes
Trade area analysis is a complex process. And while it’s tempting to use your intuition or trust a handful of key factors to lead you to the best locations, the reality is that numerous circumstances influence the viability of a trade area for your business, and even the best retailers often mishandle this process and misjudge real estate transactions.
Here are some common aspects of trade area analysis that retailers get wrong.
Conducting trade area analysis too infrequently
Trade area analysis should at least be an annual process. The more often, the better. Think about how fast cities change. All it takes is a few significant developments to completely change the value of a trade area—for better or worse. If you’re not frequently re-evaluating the trade areas you operate in and the ones you’re pursuing, you could easily wind up missing out on a major new opportunity or sitting on an increasingly poor-performing store.
This ongoing process is a critical component of store lifecycle management. It’s not just about net new stores. You should be using trade area analysis to evaluate and manage your existing portfolio and to understand the impact new stores will have on existing ones.
Using a “one size fits all” approach
When you’re trying to compare two or more things, standardization is key. So it makes sense that retailers sometimes take a cookie-cutter approach to trade area analysis, defining trade areas by using fixed radii around your locations. But it’s wrong. And it can lead you to prioritize the wrong locations and miss the opportunity of trade areas that don’t neatly fit into your specifications.
In trade area analysis, your process and criteria should be standardized, but not the shape and size of your trade areas. For example, trade areas in urban regions will always be smaller than suburban ones, where the population and activity is less concentrated. Every trade area is unique, so it doesn’t help you if your analysis restricts them to the same size and shape.
Not making the analysis specific to the retailer
If your best customers are families with young children, then single adults with no children probably aren’t relevant to your analysis. They may become relevant in the years to come, but at the moment, it doesn’t make sense to include their income, spending patterns, or mobile movement data in your evaluation of the trade area’s opportunity.
Some retailers mistakenly analyze raw population counts and other generic information about a trade area, rather than isolating the information that’s relevant to their business.
Depending on the depth of your customer data, this can get incredibly granular (and useful). You may have customer profiles that group demographics together to match some of your most common types of customers. Or you may simply have identified the demographics that are most valuable to you. As you analyze a trade area, you can examine the breakdown of each of these demographics—and ideally, the amount you could expect them to spend with you annually.
Suppose married women in their 40s spend an average of $400 per year at your stores, single men in their 20s spend an average of $20 per year at your stores, and numerous other demographics tend to fall somewhere in between. A trade area isn’t necessarily better if it has a higher concentration of married women in their 40s, but it would take 20 times as many men in their 20s to equal the spend of one married woman in their 40s.
Understanding how specific demographics contribute to the actual opportunity is crucial to making predictions about how your business could expect to perform at a location. The more you know about segments of your customer base and their overlap with the trade area’s demographics, the better your analysis will be.
As much as possible this should all be rooted in comparisons between similar locations with shared characteristics. Whatever the demographics, an airport restaurant isn’t going to perform the same as a drive-thru location. And even if two locations would fall under the same category of building, you need to use more nuanced site selection criteria to ensure that they’re actually comparable.
For example, a site that’s difficult to see and requires customers coming from one direction to cross three lanes of busy traffic isn’t going to access demand nearly as well as a more accessible, visible location. So even if they’re surrounded by comparable demographics, you can’t expect them to perform the same.
Overlooking cannibalization
Any time you’re doing trade area analysis, you have to consider the impact of store cannibalization (and if applicable, sister store cannibalization). If a new store is going to take sales from an existing location, then the opportunity represents fewer net new sales. Depending on how close a potential site is to a current location, it may not even be worth taking the time to analyze. However, at times doubling down on a lucrative trade area can be more profitable than expanding into a mediocre one. The only way to know is by estimating cannibalization.
Some retailers and consultants use a broad brush for these estimates, essentially guessing based on the overlap in each location’s serviceable trade area and the demographics that move through that overlap. But it’s more complicated than that because these locations likely aren’t equally accessible or visible, and other site characteristics affect how well the location can tap into the overlapping demand.
This is another area where retailers rely on Tango Predictive Analytics. Our AI and machine learning algorithm accounts for a wide range of criteria to build reliable models of each site, and it generates extremely accurate sales forecasts using the formulas and models that best fit the location. This dramatically reduces the margin of error and helps businesses see the true impact a new store would have on existing ones.
Forgetting the omnichannel opportunities
A new store isn’t just a point of purchase. It’s also a fulfillment center. An inventory source. And your presence (or increased presence) in a new trade area stimulates ecommerce sales. The more prominent and visible your location, the greater the effect, because the more top of mind your brand will be when people shop from home, work, or wherever else.
The idea of a trade area is changing. Thanks to omnichannel retail, it isn’t just for the four walls of the store anymore. In fact, it’s common for ecommerce customer data to play a role in trade area analysis and site selection. Placing a new store near online-only customers opens the door for them to use alternative fulfillment methods like curbside pickup and buy online pickup in store (BOPIS), and to simply shop in-person when they couldn’t before.
Of course, in order to account for the omnichannel impact a new store will have, you need tools that can incorporate the right criteria. Here’s how Tango shows you where your online customers are in relation to a trade area.
The key to effective trade area analysis
The whole point of trade area analysis is to inform your real estate decisions. It should point you to the best locations for new stores, relocations, or closures. Each decision could cost millions of dollars and years of investment. So it’s important that you get your trade area analysis right. And that takes the right software.
Some site selection software can’t incorporate all of the criteria that influences performance. Or it doesn’t have the sophistication to rule out factors that don’t matter. Or to change sales forecasting formulas based on what’s most relevant to the location.
Tango Predictive Analytics takes your trade area analysis to a whole new level, giving you the most advanced artificial intelligence, robust analytics capabilities, and ultimately the most reliable information.
Want to make the best decisions? Get the best software.
Request a demo of Tango Predictive Analytics today, and see what our tools can do for you.
