SafeGraph’s Guide to Developing a Winning Retail Strategy with Location Data

4 ways retail businesses can harness the power of location data to make smarter decisions and remain competitive with consumers.

The Brick-and-Mortar Retail Industry is Becoming More Data-Driven Than Ever Before

Retail has always been a competitive space. And as more consumers now shift their attention to the fast-growing e-commerce space—which accelerated at an even faster clip during the COVID-19 pandemic—brick-and-mortar retail businesses have no choice but to overhaul their end-to-end operations strategies. 

For one, consumer behaviors are radically different today than they were even five years ago. The pandemic has a lot to do with this shift, but in all fairness, certain consumer shopping behaviors were already evolving well before life got turned upside down. But knowing that, at least as of now, nearly 50% of consumers say they’ll continue shopping online after the worst of the pandemic is over, brick-and-mortar retailers are going to have to go the extra mile to not only win back foot traffic but also provide better and more convenient customer experiences. 

The good news: 90% of retail transactions still take place offline. And despite the challenges presented by COVID-19, retail is still poised to make a comeback because, let’s face it, people want to be out and about in public again. Which means now is the time for retail businesses to think about what the future looks like and proactively prepare for the next generation of retail. 

Accurate data will be the key for driving actionable insights around retail’s rebound in the years ahead. Location data, such as points of interest (POIs), building footprints, and foot traffic, can offer both retailers and urban planners in-depth insights for not only going head-to-head with the competition—including e-commerce—but also for developing long-term retail strategies that touch all points along the customer journey.

In this guide, we’ll show how location data can give retail businesses a competitive edge in market analysis, site selection, promotional strategy, and store planning. Simply knowing how to use this data to your advantage will give you the power to adapt your retail strategies in real-time to ever-evolving consumer and market dynamics.

>> Step-by-step guide for correlating your store data with SafeGraph Patterns data.   <<

Key takeaways at a glance

Retail businesses can use location data in powerful ways to fuel insights around:

  • Market analysis: Assessing local market dynamics at a deeper level to determine whether launching a storefront in a given retail trade area will drive long-term success. 
  • Retail site selection: Pinpointing the best location to build a new retail store, based on demographic alignment and other factors uncovered during the market analysis.
  • Promotional strategy: Identifying how best to target, reach, engage, and attract desired consumer audiences—from within a local trade area—to a specific retail location.
  • Store planning: Determining operational efficiencies for maximizing revenue potential and creating exceptional and more relevant in-store customer experiences.

Location Data is Your Secret Weapon for Retail Success

Buying into the saying, “If you build it, they will come,” isn’t a winning retail strategy. In fact, before you “build it,” there are a flurry of important decisions that need to be made to set your retail business up for success right from the start. 

Long gone are the days when urban planners could just leverage past experience and loosely quantifiable trends to inform retail site selection decisions. And while there’s always an element of “trusting your gut” involved in launching a successful retail business, location data has now become a retailer’s greatest strategic ally in this endeavor.

Let’s take POIs as an example. As a retailer, you want—no, need—to know where and how close you might be positioned to your nearest competitor. Even more, you’d want to know how long that competitor has been there because, if your goal is to steal market share, you’ll need more than just a great location to make that happen. Similarly, you should think about what other complementary types of businesses are near your proposed locations, as those, too, can either generate “recycled” foot traffic to your location or hog all of the attention for themselves. In short, understanding what POIs surround your proposed location can provide keen insights into how well your business will potentially thrive within that context. 

Taking this a step further, you can also use foot traffic data to understand how many people, including who those people are, demographically speaking, regularly visit the other retailers in the area. And then once you’ve established your location, you can use this data in multiple ways: from determining store hours to anticipating the busiest times of the day. 

Finally, at a more micro-level, you can use building footprints to plan smarter store layouts that maximize revenue-earning opportunities throughout the in-store customer experience. 

Long story short: There are a number of actionable uses for location data that can empower you to build more holistic retail strategies that have a greater potential to succeed.

Only 45% of retailers currently use location analytics to inform retail strategy, even though 74% say it’s important.

Location Analytics for Retail (ESRI)

Build Better Retail Strategies with Location Data

Running a successful retail business involves a lot more than simply picking the right location—even though that, in and of itself, is a critical factor for success. 

What you may not realize is that at every stage of a retail business’s life cycle, there is an opportunity to use location data to give it a competitive edge. Here are four specific use cases that demonstrate how location data can help retailers, urban planners, marketers, and merchandisers create better retail experiences that measurably boost the bottom line. 

1. Market analysis

While market analysis is a general best practice for starting up any new business, it’s absolutely critical for launching new brick-and-mortar retail store locations. 

Simply put, market analysis is an objective way to assess whether your business will be well-positioned to address the needs of consumers via the products and services you offer. It’s also a great way to reduce potential risks before investing too much money in making a business dream a reality. According to the U.S. Small Business Administration, a market analysis looks at the following factors that will ultimately determine your retail business’s long-term success

  • Demand: Do the consumers in your area want or need what your business offers?

  • Market size: How many consumers can your business conceivably target and reach? Is that enough for your business to break even and then drive a profit?

  • Economic indicators: Do the consumers in your area have enough disposable income to buy the products or services your business offers?

  • Location: How far is your business from where your target consumers live and is it easy for them to get to and from your store?

  • Market saturation: What other similar businesses already exist—and for how long—in the same area? What are their relative strengths and weaknesses? Does this area need another similar business? Is the competitive field already too full to break through?

  • Pricing: What are your target consumers willing to pay? If there are already competitors in an area, how does the price of the products and services you offer compare to those of your competitors? If you don’t offer a lower price, what does your business uniquely offer that substantiates the price you’re asking customers to pay? 
Catchment analysis is a great way to position your business for success within a given market.
Catchment analysis is a great way to position your business for success within a given market


For a new retail location to be successful in any market, it must be able to deliver on an unmet consumer need that hasn’t been adequately addressed by other local businesses. 

Location data can help you assess this with greater precision and accuracy. This is called catchment analysis. Retailers, market analysts, and urban planners do this by leveraging geographic information (i.e. number of households or family size in a given area) coupled with key consumer demographic details (i.e. age, income, education, and occupation) to paint a picture of where your business’s customers are most likely to come from. This alone can give you a good idea about what your true market potential looks like. 

When you combine anonymized mobile foot traffic data with third-party data sources, like those from the U.S. Census Bureau of the U.S. Bureau of Labor Statistics, you can begin to understand consumer behaviors in much more granular detail—from media consumption patterns to how local consumers perceive your nearest competitors. This kind of information can drive unique insights that can take your market analysis to an entirely new level, both shedding light on untapped opportunities as well as avoiding the risk of potential failure.

2. Retail site selection

Whereas market analysis is intended to identify your retail business’s opportunity or potential in a given area, retail site selection is about honing in on the exact spot that will drive the greatest amount of success (and foot traffic) for your business. 

As residential communities are rapidly evolving, especially within metropolitan suburbs, it is having a massive impact on the retail landscape as well as on consumer demand within a specific retail trade area. Traditional market research tactics to understand these shifts are too slow to provide actionable insights in real-time. By the time those insights are available, they’re pretty much already obsolete. 

This is where location data can really give retailers a competitive edge in retail site selection. It can help you visualize how local market dynamics have changed over an extended period of time, uncovering new opportunities and insights in real-time that wouldn’t have necessarily been apparent through more traditional or one-dimensional data sources. 

90% of retail transactions still take place offline.

CARTO

In addition to these insights, location data can also answer the following questions: 

  • What other POIs are located within the same retail trade area? 
  • What Census Block Groups (CBGs) feed into those POIs? 
  • How much travel takes place between different neighborhoods? 
  • How much foot traffic do those POIs drive? 
  • Who are the consumers going to those POIs (in terms of demographics)?
Using the Huff Model to identify the retail trade areas of five Whole Foods locations in Los Angeles.
Using the Huff Model to identify the retail trade areas of five Whole Foods locations in Los Angeles.

Once you’ve decided where to place your business, you can use location data once again to help run your business more efficiently. For starters, it can help you determine: 

  • What day of the week a CBG is the busiest
  • What time of day a CBG is the busiest
  • When people stop in the CBG at peak travel times (breakfast, lunch, dinner)
  • Where people travel from to get to that CBG
  • Whether foot traffic patterns differ on weekends versus during the work week

Why is this important? By understanding the total market dynamics surrounding your retail location, you can ensure your business is staffed up, stocked up, and ready to meet consumer demand at peak periods. So, in this way, while location data can help determine where your retail business could conceivably have the greatest revenue-driving potential, it can also help you run your business in a way that squarely addresses the needs of your local market area.

A note about retail site de-selection

In the same way that location data can help retail businesses pinpoint the best place to set up shop, it can also help inform retail site de-selection or, in simpler terms, where to close down operations. This became a go-to tactic for many retail chains during the COVID-19 pandemic. Faced with steadily decreasing foot traffic caused by various lockdown and social distancing measures, many retail businesses had to place strategic bets on the locations that had the greatest potential to continue driving value for the business as a whole. As a result, this also forced them to make the hard, yet data-driven choice to close down storefronts that were either seen as being unable to rebound in the short term or simply too expensive to keep open.

3. Promotional strategy 

While location data may not always be top-of-mind when developing marketing and advertising campaigns, much of the insights we glean today to target, reach, engage, and convert our ideal customers, via virtually every digital advertising platform, is deeply rooted in location data. 

This is essentially a logical extension of market analysis. The only difference here is that, as opposed to assessing your market opportunity and potential, you’re now needing to drive foot traffic to your retail business location. When approached in that way, it’s basically two sides of the same coin; how you take action on the data is what separates one outcome from another. 

Although there are limitless ways to use location data in marketing and advertising, here are a few thought starters to show you what’s truly possible: 

  • Create location-based audiences using visits to a specific place, brand, or category.

  • Understand which campaign actually drove in-store foot traffic by mapping attribution (conversions) in a more granular way.

  • Personalize in-app experiences, push notifications, exclusive promotions, and spontaneous recommendations based on a user’s proximity to a business.

  • Identify out-of-home (OOH) inventory and targeting based on proximity to POIs.

  • Run “conquesting” campaigns to devices in or near a competitor’s stores.
As our world gets increasingly connected through emerging technologies that become mainstream, location data will become much more prevalent as it stems from various new sources.

Insider Intelligence + eMarketer (2019)

4. Store planning 

Aside from being a well-known brand or offering consumers products and services that they truly love, it’s important for retail businesses to not only create a positive customer experience but also to ensure that the in-store experience maximizes the potential for revenue generation.

According to the location intelligence experts at CARTO, “Location data generated from WiFi networks, beacons, GPS applications, and Apple Indoor Maps makes it easier for retailers to optimize layouts and maximize efficiency, answering pressing business questions related to pricing, opening hours, and operations.” 

In other words, by understanding foot traffic patterns in stores—beyond simply understanding foot traffic to stores—retail businesses can create better in-store experiences. Similarly, they can use this information to more accurately determine: 

  • Store opening and closing hours
  • Peak or rush hours 
  • Staffing needs throughout the day
  • Best time(s) to restock the shelves
  • Inventory replenishment purchasing cycle   
  • Merchandising strategy
  • Store layout and design

The big takeaway here is that location data can be used in a number of practical, functional, and immediately actionable ways that can help a retail business run more efficiently. 

>> Step-by-step guide for correlating your store data with SafeGraph Patterns data.   <<

SafeGraph Places Data for the Win

The SafeGraph Places dataset, updated monthly for utmost accuracy, provides the in-depth POI, building footprint, and foot traffic data you need to build winning retail strategies. 

  • Places includes base information—such as location name, address, category, and brand association—for POIs where people spend their time or money.

  • Geometry offers building footprints for POIs derived from spatial hierarchy metadata.

  • Patterns data measures hourly foot traffic to and from POIs, along with aggregated anonymized mobile device activity and useful demographic data to help determine, with greater precision, how often people visit certain POIs, how long they stay, where they came from, where else they go, and more.

Combining the SafeGraph Places datasets with first-party retail data, retailers, urban planners, and market analysts can visualize catchment analysis, understand market penetration, and create great maps to tell compelling data stories.

SafeGraph Patterns data for restaurants and QSRs in Toronto, CA.
SafeGraph Patterns data for restaurants and QSRs in Toronto, CA.

Correlating SafeGraph Data with Third-Party Transaction Datasets

These six steps are covered in more detail in our technical workbook, so be sure to check that out when you’re ready to tackle this on your own. For now, though, here’s a quick rundown:

  1. Process, clean, and explode SafeGraph Patterns data.
  1. Adjust SafeGraph Patterns data for known sources of methodological bias (both at the monthly and day-to-day levels).
  1. Join SafeGraph Patterns data with a third party transaction dataset.
  1. Encode common sources of known variance based on historical trends.
  1. Use regression analysis to control for historical and methodological sources of variance to optimize the predictions of transaction data using SafeGraph Patterns.
  1. Demonstrate that models using SafeGraph data perform better than models relying on historical trends only. 

A few things to keep in mind

Be sure to keep these things in mind as you go through this process on you own:

  • Control for methodology: SafeGraph Patterns data is collected from a range of sources over time. To use this data to compare different time points, you must take into account the underlying changes in data collection. If you don’t control for methodology changes over time, it may be hard to produce strong correlations with real-world behavior.
  • Regression: Regression analysis is a useful tool to control for changes in methodology. Applying this within your foot traffic analysis will yield more accurate results, helping to isolate the true signal in SafeGraph data and separate it from variance, as otherwise explained by changes in methodology (i.e. changes in sample size).
  • Control for variability: Controlling for other known sources of variability in retail consumer behavior (i.e. day-of-month, month-of-year, popularity of a given venue) can make your analysis more predictive. Using a combination of historical data, methodology controls, and foot traffic signals can yield high correlations with transaction data.

Make Better Decisions in Retail with Location Data 

Location data is revolutionizing the retail industry for the better. Not only can it help retailers assess market dynamics and identify the best places to set up shop, but it can also enable them to run more targeted and effective promotions while, at the same time, create better in-store experiences that drive customer loyalty and maximize revenue potential. When looking at it this way, the power of location data to transform retail businesses is truly limitless. 

However, we understand that analyzing location data might seem intimidating at first, especially if you haven’t used it like this before. But it doesn’t have to be. With the right tools and techniques in place, like those we’ve shared here, location data can give your retail business the competitive edge it needs to be successful in any local market of your choosing. And if you’re not sure quite where to start, our team is always here to help!

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