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Site De-Selection: Choosing the Right Stores to Close Comes Down to Data

June 23, 2020
Ryan Fox Squire

As chain-store businesses now confront the reality of closing stores in the aftermath of COVID-19, SafeGraph data can help inform these tough decisions 

It’s no secret that COVID-19 has changed the business landscape across the United States in pretty massive ways. This is becoming even more apparent as states begin to reopen, some more aggressively than others, to get the local economy back on track again. 

Unfortunately, not all businesses have fared equally in the face of the novel coronavirus health and economic crisis. While grocery stores, for example, experienced some pretty massive spikes at the height of the crisis due to panic-buying—and have continued to thrive as many dine-in restaurants are still slow to reopen, the retail industry has been dealt a serious blow.

Daily percent change in SafeGraph foot traffic for various grocery store chains, relative to February 26, 2020, show the extent of panic-buying.

This has forced many businesses and commercial real estate companies, even those that were in rapid-growth mode pre-pandemic, to weigh the pros and cons of closing down operations in the absence of consumer demand. This has been especially the case for large chain-store businesses—from restaurants to gyms to specialty goods stores (and beyond).

Because a return to “normal” is taking considerably longer than anyone anticipated, they have no choice but to cut costs. And what better way to do that than by closing store locations that risk continued under-performance even as the economy reopens. 

COVID-19 has changed the business landscape in fundamental ways

Many of the “business as usual” rules that applied pre-COVID-19 have gone out the window, causing businesses to re-think their consumer engagement models for post-COVID-19 times. 

Let’s take shopping malls as an example. For years, the concept of “anchor” department stores has been the prevailing trend. However, with the retail industry in free fall, will these anchor stores still be relevant—or even still in operation once surrounding businesses begin to reopen? 

Change in year-over-year foot traffic (as measured by SafeGraph Patterns) to major department store brands in the U.S.from January 2019 through March 2020 (where 100% represents no change at all).

And even if they can weather the current storm, will they bring in the foot traffic required to keep the shopping mall model thriving? Or will consumers uphold new social distancing habits until there’s confidence that being in public spaces won’t put them at risk for contagion? 

The same can be said for restaurants. With maximum capacity now as low as 25% and strict rules in place to minimize virus spread, many restaurants, especially during the months of total shutdown, relied squarely on curbside pickup or delivery to keep their businesses afloat. 

Dine-in restaurants, even those on the higher end, suddenly found themselves competing head-to-head with fast-food restaurants. And while this kind of competition may have been unfathomable to imagine pre-COVID, these restaurants have had to reexamine their entire business model and consumer engagement strategies to stay top-of-mind. 

This has been especially pronounced during lunch rush hours; with more people now working from home, there’s a lot less dining in and a lot more delivery ordering. And what do you think people are ordering for delivery most: fast food or fine dining?   

There are many more examples like these where tried-and-true business models have been turned upside down, disrupting industries that, for years, have run like clock-work. In these uncertain times, we can lean on data to help us make tough, yet informed business decisions.  

To close or not to close, that is the (data-driven) question

For those businesses faced with the unfortunate reality of scaling back as the only option for pre-pandemic survival, data is the key to making sound decisions as to which stores to close and which to keep open. This isn’t so much a question of how many stores to close as it is a question of the right stores—in this case, the biggest under-performers—to close without negatively impacting over-arching revenue and profits. 

Knowing this is a delicate balance that could, if done incorrectly, negatively impact a business’s balance sheet, the key priority here is avoiding the closure of the wrong stores and, as a result, inadvertently letting underperforming locations sneak past the proverbial chopping block. 

Truth be told, this question of site de-selection involves many of the same factors that go into site selection. It’s a combination of knowing the following: 

  1. Where those Points of Interest (POI) are located? 
  2. What Census Block Groups (CBG) feed into those POIs?
  3. How much travel takes place between various neighborhoods? 
  4. How much foot-traffic those POIs drive? 
  5. Who, demographically-speaking, are the consumers going to those POIs? 

Understanding customer behaviors and characteristics at a hyper-local level

SafeGraph’s Neighborhood Patterns and data on brick-and-mortar customer demographics (powered by SafeGraph Patterns) is the fuel that can help businesses answer these questions and make more informed decisions around site deselection. 

An example of customer demographics insights in action, powered by SafeGraph Patterns data, showing relative household income  for Target vs Walmart, aggregated over all locations nationally.

The image above is a perfect example of our customer demographics insights in action. Here, we can see at a quick glance that Target caters to a more affluent demographic than Walmart across the United States. Coupling this information with SafeGraph Places data and our Neighborhood Patterns for every Census Block Group can help us narrow down our focus to a hyper-local, store-by-store level across over six million POIs. 

This can also help answer the following questions, critical for assessing a POIs success potential:

  • Which day of the week is a CBG the busiest? 
  • What time of day is a CBG the busiest? 
  • When people stop in for breakfast, lunch, and dinner within a given CBG, where are they traveling from to get to those locations? 
  • How do weekday patterns vary, if at all, from weekend patterns, within a given CBG? 

The value here is simple: having access to knowledge about a specific store’s customers (via anonymized mobile location data) can go a long way towards helping businesses identify which stores have the greatest opportunity of driving increased demand and rebounding in both the near- and long-term as well as which have the greatest future revenue-generating potential. 

Long live the Huff Model of site selection

This goes hand-in-hand with retail trade area analysis, helping businesses understand where people live, work, and shop in relation to commercial businesses. Part of this involves knowing where customers are traveling from as well as calculating the average drive times for getting from point A to point B. The Huff Model then takes this information and combines it with data about store size and relative distance from shoppers to predict a given store’s “attractiveness”: in other words, the likelihood of a consumer one store over another in the same area. 

Just recently, researchers have developed an extension to the Huff Model: the time-aware dynamic Huff Model or “T-Huff” for short. This is a way of weaving in “time” as a variable into understanding foot traffic patterns. Whereas the traditional Huff Model could only identify, at a high-level, a store’s relative attractiveness, the T-Huff provides a way of analyzing what days and times stores are most trafficked. 

Huff Model's trade area analysis output for 5 Whole Foods Market stores in Los Angeles.

As you can imagine, being able to understand foot traffic with this level of granularity pre-pandemic can shed incredible insight to how specific stores may operate again once life starts to get back to a more normal rhythm. In many ways, while this could have also easily informed site deselection under normal circumstances, during these COVID-19 times these insights set the bar for success higher and make it easier for businesses to place bets on where there are the most viable business-growth and business-rebound opportunities. 

Data and site de-selection go hand-in-hand

Long story short: data is the key for making informed decisions about both site selection and site deselection. At a difficult time like this, however, having access to highly accurate data is essential for providing insights at the hyper-local level that can help chain-store businesses and commercial real estate developers make solid assessments about how to navigate the ever-changing business landscape in a soon-to-be post-COVID era. Deciding which stores to close down is never an easy decision. When you’ve got clean, accurate, and precise data to back up your decision, it can help make the sting feel a bit less painful. 

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If you are with a for-profit business, please contact our team today to learn how these datasets can help your business navigate this unfolding health and economic crisis.

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