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How to Use POI Data for Catchment Area Analysis

March 14, 2023

Tips for working with SafeGraph Places data to improve retail site selection 

Choosing the right location for a retail business or other commercial property is, in many ways, the key to its long-term success. A “bad” location—for example, a storefront in a secluded area without any surrounding businesses or one that’s not easily accessible from main roads or highways—can spell doom and gloom right from the start. This is why businesses and real estate planners do catchment area analysis, also referred to as trade area analysis, before making any investment in the purchase of property or land.

There are a number of different approaches for running catchment area analysis for site selection. That’s why we thought we’d let you in on a little secret about how you can use POI (point of interest) data to not only fuel your efforts around catchment area analysis but also glean actionable insights around how to engage and retain customers as well as create better overall customer experiences that local consumers actually want and need. 

So if you’ve never considered using POI data to inform your retail site selection decisions, in this blog we’ll show you how you can do this effectively with the help of SafeGraph Places data.

Why POI data makes sense for retail site selection

Before we get into the nitty-gritty of using SafeGraph Places data for catchment area analysis, it’s important to first set some context around what retail market planning teams care about as they make decisions about either opening new locations or closing underperforming stores. 

The primary reason a market planning team will do catchment area analysis for site selection is to ensure a new store location can perform at or above expectations—typically, in terms of store-level EBITDA or other key financial metrics. To do this, they may assess factors, such as:

  • Coverage of POIs in a particular category and region
  • Potential market share within the catchment area or region as of day one
  • The likelihood of ongoing consumer demand within the trade area or region
  • The volume of key competitors already doing business in the surrounding area
  • Ease of accessibility to the store location (including parking)

In short, before a business makes an investment in a new store location, it’s critical to understand the risk-to-reward ratio of doing so. Obviously, no business comes without its risks; however, by leveraging accurate and up-to-date data to create an educated hypothesis around a store’s potential for long-term success, it’s much easier to minimize any potential risks head-on. This is where using POI data for running catchment area analysis can really come to the rescue. 

Using SafeGraph Places data for catchment area analysis

For the purposes of this exercise, say you’re considering opening a new retail location, either a restaurant or a clothing boutique, in one of two fast-growing Austin neighborhoods: one just south of the city center (South Congress) and the other in the city’s northern suburbs (Domain). 

By plotting the SafeGraph Places dataset into a mapping tool like CARTO, you’ll get a visual representation of the two trade areas, including buffer areas across the neighborhoods as well as the approximate walking distance between places in those areas. 

In the example map below, you can see via the category tags that the neighborhood in North Austin has a high density of clothing and shoe stores whereas the South Congress area is more restaurant-centric. So if you’re looking for a location for a new restaurant, you might have a lot more success if it’s in the heart of a retail district that caters to a constant influx of shoppers—and just so happens to have less competition from nearby restaurants. On the other hand, if you’re looking to open a new retail boutique, you might want to consider the South Congress area because there are a lot of eateries around that can amplify the shopping experience for consumers.

Then again, in both scenarios, you’ll want to pay close attention to what kind of restaurants or retail boutiques are already in the area because, in spite of a higher concentration of certain POIs, there might not actually be a high concentration of the same type of restaurant or retail shop you’re looking to open. That in and of itself could create a unique opportunity for success.

In any case, the big takeaway here is that POI data can shed a tremendous amount of light on how to not only address the wants, needs, and expectations of the target demographics (aka, people) who live in or frequently travel to a specific retail trade area but also how to use your new restaurant or retail location to provide added (and differentiated) value in those areas.   

Now, let’s take a closer look at what kind of information you can find on this map. By hovering over a pin for each POI shown here, you get access to a wealth of metadata, such as:

  • POI type (full-service restaurant, limited-service restaurant, clothing store, shoe store)
  • POI name 
  • Street address
  • Category and sub-category
  • “Opened on” date 
  • Geolocation (latitude and longitude)
  • The number of similar POI types within a 0.5-mile radius

This data allows you to see at a quick glance the key differences of the two trade areas, which can also help answer important questions and make site selection decisions a lot easier: 

  • Are there any coverage gaps? 
  • Does coverage overlap in any way?
  • Where are competitors located in relation to the proposed location? 
  • What is the concentration of similar POI types in a given retail trade area? 
  • How far is the location from other shops, restaurants, public transportation, etc.? 
  • How many new stores have opened in the past year (area growth rate)?

Answering these questions (and more), therefore, allows you to assess the potential of one trade area over another, from a variety of different angles and key considerations, in order to hone in on a new store location that will have the highest probability for success as of day one.

For many market planners, the most important factor is to settle on a location that caters squarely to a business’s target audience, where there are fewer similar store or restaurant types—including direct competitors—in the immediate vicinity while also being in close proximity to other complementary shops, restaurants, and commercial or public buildings to benefit from the organic residual foot traffic that exists within a given neighborhood. 

Finding that sweet spot is not always easy. However, by visualizing SafeGraph Places data in this way, it becomes a lot easier to understand the “big picture” market dynamics of a trade area.

Enhance retail site selection with SafeGraph Places data

While there are certainly a number of factors beyond POI data alone that can go into the catchment area analysis process, it goes without saying that using POI data can enhance market planning and drive better, more informed retail site selection decisions in significant ways.

This is made even more effective by tapping into the power, accuracy, and granularity of the SafeGraph Places dataset, which now covers 41M+ POIs, 10K+ brands, and 400+ categories in more than 220 countries and territories. Not to mention, every POI in our dataset includes detailed metadata, including precise geocodes, store IDs, category tags, open hours, open and close dates, nearby public transportation, and spatial hierarchy. So whether you are doing catchment area analysis or want to leverage location data for retail and real estate analytics, the SafeGraph Places dataset has got you covered (no pun intended!). 

The moral of this story is fairly simple: Long gone are the days of relying solely on old, traditional methods for doing catchment area analysis successfully. Now, you’ve got a new and, dare we say, more effective and incredibly comprehensive way to fuel retail site selection decision-making with a higher degree of confidence, thanks to SafeGraph Places data.

Use SafeGraph Places data to take catchment area analysis to the next level.

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