The Big Wake-Up Call: Re-thinking the Entire Playbook for the Quick Service Restaurant Industry

How the pandemic has fundamentally reshaped the future of the QSR industry    

The pandemic hasn’t been kind to the restaurant industry—and the fun’s far from over.

As a preface to what you’re about to read, we promise not to drone on and on about the pandemic. We get it, you’re tired of talking about a topic that has dominated pretty much every part of our lives over the last two years (and counting). 

But now that the dust is finally settling—or, at least, as one would like to believe—we can’t help but take a step back to see how this global human experience turned many industries and businesses upside down. Some for the better and, unfortunately, some for the worse. 

The quick-service restaurant (QSR) industry is a perfect example of an industry that has evolved from the inside out over the course of the pandemic—wherein the pre-pandemic status quo is a far cry from what the future of the industry looks like. And QSR business owners, from ‘mom and pop’ restaurants to multi-national chain restaurants, need to accept that reality head-on.

The truth is, there’s no returning to “normal” anymore (whatever that really means). Because the pandemic has ebbed and flowed for such an extended period of time, the QSR industry has had no choice but to adapt to new consumer behaviors—and change course entirely. 

But what does that actually look like? Glad you asked. To get an inside scoop on the evolution of the QSR industry during the pandemic—including the major pivot the industry must make in order to thrive from here on out—we had an in-depth conversation with Mike Lukianoff, data science entrepreneur and QSR industry guru, to map out the new QSR industry playbook. 

What you’ll learn by reading this

  • How the pandemic expedited disruption across the entire QSR industry.
  • How evolving consumer behaviors forced QSRs to build new business models rapidly.
  • Why the future of the QSR industry will rely heavily on having access to the right data.
“The restaurant industry has completely transformed over the past two years. The way we analyze it must change, too. What we know is that the old playbook simply isn’t working anymore.”

– Mike Lukianoff,
Data Science Entrepreneur & QSR Industry Guru

Pushing the long overdue ‘reset button’ on the QSR industry

The last thing we want to do here is to relive the experience when the pandemic first took hold. So, we’ll keep this part short and sweet. 

But here’s what happened in a nutshell. Everything shut down. Businesses. Schools. Restaurants. Pretty much any public space imaginable (except for grocery stores and banks). And for those people who could do their jobs remotely, the home became the new office. 

This massive shift in where people were now spending the bulk of their time—aka, at home—essentially turned business districts and commercial centers into ghost towns overnight. The big problem here, however, is that much of the QSR industry was built around people not being at home all the time. Generally speaking, the assumption was that people leave their homes in the morning—to go to work, school, the gym, the doctor, and so on—and spend their hard-earned dollars in the businesses dotted along their daily journeys. 

For example, people who commuted to work in the morning would typically grab their morning coffee (and pastry, let’s be honest) before heading into the office and then, more likely than not, go to a nearby restaurant to grab food for lunch, whether take-out or dine-in. There’s a good chance that they would run errands at other local businesses, too. And of course, after a long day at the office, the occasional happy hour was certainly well-deserved. So, suffice it to say, a big part of the QSR ecosystem revolved around restaurants being accessible to where people spent most of their time. This is the primary goal of retail site selection and trade area analysis. 

That being said, the industry was not prepared for the massive, pandemic-induced migration to remote work whatsoever. This is why, according to the National Restaurant Association, as many as 12% of restaurants closed over the course of the pandemic. Interestingly enough, these weren’t necessarily the restaurants that many QSR businesses would have, perhaps, anticipated closing—based on past performance models—prior to the pandemic. 

“The QSR industry was built on the premise that consumers will leave their homes daily to transact with their local community in some way.”

– Mike Lukianoff,
Data Science Entrepreneur & QSR Industry Guru

The QSR industry was already at an inflection (ahem, breaking) point

Every industry goes through its own fits and starts at some point. The QSR industry was no exception to this rule. In fact, it could not continue down the ‘status quo’ path without reaching a breaking point. The pandemic merely expedited this, just in slightly unexpected ways. 

So, what do we mean by “breaking point” here? Generally speaking, the industry was already overbuilt. For example, retail trade areas zoned for restaurants—those primarily catering to daytime commuters—were oversaturated with restaurants well before COVID-19 reared its ugly head. Whereas residential areas still remained restaurant ghost towns. This created a huge unbalance making it tough for the few restaurants in residential areas to keep up with growing (pickup and delivery) demand. But truth be told, had the pandemic not happened, it was pretty much only a matter of time until the industry “rightsized” itself naturally.

But still, the pandemic created a new dynamic that really had nothing to do with rightsizing and quite a bit more to do with an unplanned shift in consumer behaviors. As lockdowns became the new normal and retail (including restaurant) locations had to cease in-person business operations, consumers looked to digital to keep their lifestyles afloat. 

The restaurants that were able to make this digital pivot quickly—which just so happened to be the bigger chains with greater tech and financial resources to tap into—were much more successful at keeping the lights on. Additionally, those with drive-thru or home delivery offerings were able to weather the storm as well. As a matter of fact, during this time, digital ordering grew from less than 15% of QSR sales (pre-pandemic) to over 35% since COVID-19 became a household name. And now, it’s projected to reach 50% by 2025!

Unfortunately, on the other end of the spectrum, those smaller brick-and-mortar restaurants, typically those of the ‘mom and pop’ variety, simply couldn’t keep up over the long term. True, many were able to take advantage of the initial groundswell of “support for local businesses” that happened when the pandemic first hit. However, as consumers became increasingly fatigued with lockdowns and other security measures droning on, they started to lean into their own self-interest (versus community interest) and began seeking out more convenient options. This swung the pendulum back towards the big chains that were able to leverage technology to streamline the customer experience as a tactic for winning back business.

Now, under normal circumstances, the expanding and contracting of the entire QSR ecosystem, especially in highly saturated trade areas, would have been a function of demand dynamics simply doing their thing. Though, when faced with unpredictable demand irregularities that had, at the time, no real end date in sight, traditional momentum models based on long-term trends fell apart. This basically threw the entire industry upside down in one fell swoop.

The proverbial ‘center’ of the QSR industry shifted in a big way

Traditionally, the heart of the QSR industry revolved around business or commercial centers—in other words, the places near where people would go to work or run errands every day. But the demand that was once created by daily commuter traffic disappeared almost overnight during this so-called “residential shift.” This threw big cities an unexpected curveball. 

With people no longer going to the office daily, restaurant owners had to ask themselves some serious questions: Were these locations (in business districts) even needed anymore? How long can we hold out—or buy time—until people start going back to work again? If we stay open, even with the help of tech or delivery offerings, where will our customer base come from? 

Although some business districts have started to rebound as return-to-work restrictions have finally started to relax across the country, the reality of long-term hybrid work is still very much on the table. If the pandemic has taught us anything, it’s that the myth that work can’t happen when people work from home got completely debunked. This may explain why, in spite of employees slowly returning to the office, occupancy is still hovering at 40% (give or take). 

Now, here’s the longer-term problem of this shift. With many office-based employees now used to not having to commute to work daily—much less during this frothing “great resignation” job market—employers have had to make hybrid or full-time remote work an available option in order to either retain current employees or attract new talent for open roles. 

This has essentially caused the center of the QSR industry to officially move away from bustling business districts toward the places where people live. This shift has also created new demand dynamics—and pricing pressures—that never existed before. Simply put, this has challenged the QSR industry’s ability to thrive in the face of a constantly changing consumer landscape.

But this notion of “thriving” varies significantly based on where restaurants are located. On the one hand, while the restaurants in business districts have buckled under the pressure of reduced foot traffic, restaurants in residential areas, on the other hand, have had a renaissance that continues to build steam. Either scenario hasn’t necessarily been easy to adjust to.

“The ongoing impact of the pandemic has called into question the future of tens of thousands of QSRs zoned in ‘work/shop’ areas. Fixing this supply-and-demand disequilibrium could last well into 2023.”

– Mike Lukianoff,
Data Science Entrepreneur & QSR Industry Guru

Uncertain times make pricing decisions a moving target

The QSR industry has experienced various bouts of whiplash throughout the pandemic’s peaks and valleys. This has thrown traditional pricing models for a loop, especially in the face of labor shortages, supply chain constraints, and the inflationary environment we’re living in today. 

The question most restaurant owners are asking themselves now is, “How do I manage or maintain my cost structure without the potential risk of losing my customer base?” 

Answering that question isn’t necessarily easy. While there’s still a lot of great data (we’ll get to that soon) available to inform a restaurant owner’s decision-making, deriving a true sense of price elasticity in this unprecedented environment is much less reliable than it used to be. Though, not impossible. It just requires the QSR industry to build a new price elasticity model. 

The QSR industry needs to build a new value equation 

Had consumers not shifted their entire sphere of existence around where they lived during the pandemic, this would likely be a non-issue. Yet, when business districts suddenly had zero demand and residential areas, which were not previously zoned for as many restaurants, went into overdrive, the very nature of zoning had to shift as well. 

Until that happened, consumers had very few nearby options to choose from and, as a result, were probably more willing to pay a premium in order to get access to more restaurant choices (or at least, those with a larger delivery radius). Though, as demand increased in residential areas, the number of new restaurant choices naturally started to pop up. This essentially forced the QSR industry to build a new value equation for pricing decisions in this new environment.

This is why restaurants must now approach price elasticity from both a product and location standpoint—and then develop assumptions around what the price “inflection points” would be based on the spending threshold of the consumers near those trade areas. In many ways, this new value equation is akin to doing a tightrope walk around what increase or decrease in prices would potentially cause consumers to change behaviors in some way (for the good or bad). 

The reason why you have to take both product and location into consideration here—thereby creating a truly localized approach to price elasticity—is because different trade areas could have radically different price inflection points. For instance, if you took into account location alone, you might assume that a trade area with few competitors would be ripe for price increases. But when you layer on the average income levels of the people living in or near that trade area, and perhaps find that those consumers don’t have the disposable income to justify price increases, any price hikes could backfire and cause consumers to consider other options. 

But then there are the factors of experience and convenience as well. If a restaurant offers a better overall customer experience, including online ordering and low-fee delivery service, along with a high-quality food product, then consumers may be willing to accept reasonable price hikes as long as the positive experience offered by the restaurant remains intact. 

Clearly, there’s still a lot of guesswork at play here because we haven’t firmly settled into this new industry dynamic. And because historical pricing models, which were deeply rooted in pre-pandemic demand dynamics and consumer behaviors, can no longer serve as a reliable blueprint for future planning, price elasticity will continue to be a moving target until the dust of the post-pandemic aftermath and so-called run-away inflation settles.

“Traditional pricing models rely on stable demand trends for price elasticity and other demand metrics to work. Wild variation in demand has made this kind of modeling obsolete in the near-term.”

– Mike Lukianoff,
Data Science Entrepreneur & QSR Industry Guru

Location data is the key to making sense of it all

It’s clear that we can’t think about trade areas in the same way anymore. Although a restaurant’s location is still an important part of the value equation, the remote-work migration to residential areas has created a need for restaurants to be more aware of who their customers are as well as where they are coming from (i.e. Census Block Groups). This enables QSRs to group “like” restaurant locations by demographic cohorts instead of by retail trade area alone.

But it doesn’t stop there. Because we are still operating in uncharted waters, it’s important for these businesses to look at competitor pricing (within the same trade area) as well, in order to understand what wiggle room there may be around purchase dynamics. And it’s only when you start tiering restaurant locations in this way that you can begin to make calculated assumptions about how certain locations can manipulate pricing based on various “opportunity” variables. 

Speaking of variables, there are two kinds that need to be taken into account for modeling:

  1. Static variables

These are the things surrounding a restaurant’s location in a trade area that, in some cases, haven’t changed in over 20 years but, as a result of the pandemic, now may look slightly different for the first time in a very long time. QSRs need to reassess what trade areas look like today as well as the new kinds of customers frequenting those locations (as in, where they are coming from, whether it’s from home or from work).

This is basically the equivalent of pushing the ‘reset button’ around our understanding of trade areas and how they operate today. To derive these insights, you need access to high quality, granular, and accurate location, polygon, and foot traffic data (aka, SafeGraph data) coupled with up-to-date Census Block Group (CBG) data. Combined, these can paint a picture of what any given trade area of today really looks like.

  1. Dynamic variables

This is basically anything else that may fluctuate rapidly—much less in this atypical business environment we’re living in—or has the potential to provide more “texture” around the customers who visit a specific restaurant location. This could include loyalty program-, restaurant-, and marketing-related data as well as virtually any other data sources that can add a nuanced dimension to the static variables above.

In the past, many QSRs relied on momentum revenue models to predict future patterns. But now these, too, need to be questioned, with a specific focus on capturing “error” on a daily basis—whether around revenue, supply chain issues, and so on. Unfortunately, many models fail to incorporate these outside factors into the mix and, thus, have now become somewhat out of touch with real-time consumer (demand) dynamics.

The long story short is actually quite simple: The only way to make sense of this ever-changing environment is to lean into all of the data we have at our fingertips—especially location-based data—to create new awareness and understanding of how the QSR industry can get its footing and thrive (with a greater degree of confidence) in this soon-to-be post-pandemic world.

“SafeGraph, when used in tandem with restaurant data, competitor pricing data, and other demographic and commercial data, can build a more accurate measure of store demand and potential pricing power.”

– Mike Lukianoff,
Data Science Entrepreneur & QSR Industry Guru

So, what next? A more data-driven QSR industry.

By now, it should be clear that the business model for the QSR industry not only had to change in the face of the pandemic but also must continue to evolve and adapt itself to ever-changing consumer behaviors and demand dynamics. The truth is, prior to the pandemic the business model was already dying a slow death. It needed to be rethought and resuscitated from the inside out. Unfortunately, it took a global ‘resetting’ event to put the wheels into motion. 

Now, under normal circumstances, the forced change that the QSR industry experienced during the pandemic is something that, perhaps, would have taken 20 years to come to fruition. But the industry didn’t have 20 years to pivot and, therefore, had no choice but to embrace compressed transformation (especially of the digital sort) in merely two years. Of course, this hasn’t been easy for anyone involved, consumers included, but nonetheless, it has signaled an opportunity to create a stronger, more resilient, and more adaptable QSR industry of the future.

What does that look like? It’s still anyone’s guess because, truth be told, we’re all still connecting the dots as we work through this period of unprecedented change. However, what’s clear is that the industry as a whole is now reinventing itself through the lens of innovation and automation. Not only will this create better—and dare we say, more efficient—customer experiences, but it will also set the foundation for a more sustainable QSR industry. This is the key to being able to weather whatever other headwinds we may experience down the road. 

But none of this is possible without using high-quality data to inform decision-making. As we explained earlier, location-based data is an absolutely critical part of this equation. However, layering other relevant data sources on top of it is what will allow us to tease out cutting-edge insights at the local level that can fundamentally transform (for the better) how QSRs make decisions around site selection, pricing, product variety, inventory, and more with confidence. 

While massive change is always a challenge to work through, the opportunity it leaves in its wake is undeniable. This is the QSR industry’s time to shine in the (data-driven) spotlight.

About SafeGraph

SafeGraph is a data company that builds datasets on the physical world for leading companies like ESRI, Domino’s, Sysco, and Jefferies. Our high-precision Places dataset covers business listings and POIs for any brand, anywhere in the world, in addition to building footprints and consumer behavior data.

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