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Retail GIS: 5 GIS Map Examples for Retailers

Geographic Information Systems (GIS) visualize data points across an interactive map, letting you plot and analyze multiple layers of information across geographic locations. In retail applications, you can often sort and filter the data by time, demographics, and other relevant variables, making it a powerful tool for discovering insights within a given market or trade area.

While this technology has applications across a wide spectrum of industries, GIS mapping is particularly crucial during the site selection process for retail operations.

Tango Predictive Analytics combines advanced GIS tools with robust data sets and advanced machine learning algorithms, empowering you to accurately and efficiently forecast performance and locate the best sites for your business.

In this article, we’ll look at five examples of GIS maps that help you evaluate potential retail locations.

5 example GIS maps used in the retail industry

Every layer of data you apply to a map can help you glean new insights about potential markets and stores. A few important examples of data that retailers should plot on a GIS map include crime risk, traffic patterns, demographics, competitors, and points of interest.

1. Crime risk

Placing your store in a location with high levels of crime not only puts your property and your employees in danger, but also makes customers think twice before coming to visit. If the city, trade area, street, or facility in which you operate feels unsafe or untrustworthy, it’s a natural deterrent from your business.

Although you can usually look up crime rates for a given city, these often apply broadly to a large area. It’s much better to have precise crime data down to a specific location. You don’t want to end up on the one bad street corner in an otherwise safe neighborhood.

Tango partners with PlanetRisk, a leader in risk intelligence and analytics, to access their CrimeRiskSM scores—which identify and predict risk in precise geographic pockets. Their advanced modeling techniques show long-term trends, seasonal fluctuations, and changes in crime patterns. They break down crime risk by nine main categories and additional sub-categories, helping you see not only that risks exist, but exactly what kind of risks they are.

2. Traffic patterns

Traffic patterns determine how easy it is for customers to visit your store. The closer you are to their regular commutes and trips, the more likely they are to pay your location a visit. You don’t just want to be in an area where people exist on paper—you want to operate where they actually spend their time.

You’ll want to know not only how much traffic an area receives, but when it’s heaviest, which direction it’s heading, where people are coming from, how long they stop at a given intersection, and what percentage of traffic is from a transient population.

By plotting all of this onto a GIS map, you’ll get a much fuller picture of the opportunity each location represents.

3. Demographics

It isn’t enough to have traffic in your area—that traffic has to be made up of people likely to visit your store. If, for example, you opened a location alongside a truck route, you could have heavy traffic going by all day long—but unless your business caters to truckers, you might not see much of that traffic translate into customers.

Add demographic data to the map to learn not only how many people are nearby, but who they are. Use the demographic criteria that defines your customers, and apply it to both the local population density and the traffic that passes through. This will let you see how present your target audience is in the area, helping you make better decisions at an individual store level as well as at a national level.

Not sure who your customers are? No problem. Some industries are entirely dependent on third-party surveys and other resources to collect customer data. Using a tool like Tango Predictive Analytics, you can learn exactly which kinds of people patron your stores through localized geofencing. Geofencing tracks mobile movement data within a defined area—such as a small radius around your store. It isolates anonymized demographic information for the people who cross the geofence, giving you a clear cut way to collect and analyze customer data.

4. Competitors

The presence of nearby competitors will impact your sales forecasts, but it doesn’t necessarily mean you should rule out a given site. With GIS, you can add your competitors to the map, and visually represent their serviceable areas. This lets you see how each of their serviceable areas overlap with each other’s and your own.

Add demographics data and use geofencing to gain an estimate of the business any location will receive. This gives you the big-picture understanding of what nearby competitors actually mean for your business, helping you avoid missing out on a potentially great location.

Competitor locations are also a critical component of a retail void analysis. Rather than starting with a potential location and examining the nearby competitors, you start with a larger area (like a city), and measure the room it has for more businesses like yours by comparing it to another city’s business-to-population ratio. This can help you find prime opportunities others have missed.

5. Points of interest

Competitors are just one of many nearby factors that affect your sales. Schools, tourist attractions, hospitals, shopping malls, hotels, and any number of other points of interest (POIs) can generate demand, drive commerce activity, and attract particular kinds of people.

You can uncover insights about which types of POIs improves store sales when nearby by plotting a GIS map of POIs around your existing locations. Look for commonalities around your best performing stores to see if a pattern emerges. You may already be aware of complementary businesses that tend to operate near your top stores. Then plot those relevant POIs on your GIS map to see how prevalent they are around sites you’re considering.

Good GIS software will also let you factor for seasonality, since some POIs are only relevant during certain times of the year. For example, a beach town might see many of its POIs close down during winter months.

Make better location decisions with Tango

Tango Predictive Analytics uses advanced GIS tools and data sets to help you visualize and analyze markets. Create stunningly accurate sales forecasts that combine our data, your custom site selection criteria, and machine learning—empowering you to more reliably compare opportunities. With our decades of retail location modeling experience, you’ll be equipped to develop smart location strategies and make the best real estate decisions.

Ready to see what our predictive analytics software can do for your organization?

Schedule a demo today.