As we continue to monitor this unprecedented shift in consumer movement, one theme has remained consistent in our analysis of the dining sector: the decline has affected its subcategories unequally

Casual restaurants, with their focus on dining in, have suffered more significantly than fast food chains. However fast casual — brands like Chipotle, Panera, and others — has been a bit of a wild card. Their dine-in to take-out ratio is more balanced than casual dining, but our data shows evidence that certain location’s reliance on office lunch traffic seems to have left them more exposed to COVID-related changes than others. With a (currently empty) office in Midtown NYC, our team is familiar with the ritual of deciding what to eat for lunch: should we order in, go for a walk, stay nearby?  In contrast, our lunch-time choices are now limited to what’s in the fridge, or available for local delivery/pickup. We decided to dig into this dynamic to see if we could quantify the “office lunch effect”.

For this analysis we turned to our Dynamic Trade Areas product. This proprietary dataset dynamically defines a trade area for an individual venue by analyzing the areas visitors to the venue originate from – most commonly, residential and work location. This is determined by matching where a device regularly dwells to a census block group. Rather than simply drawing a standard 5 mile radius around a restaurant, we can map in detail the precise regions that support that specific business location.

Our Dynamic Trade Areas can be defined by selecting where aggregate customers live or where they work. By comparing the work-based and residential-based trade areas we can find restaurants who are over-reliant on workplace traffic, and will need to ask customers to travel significantly further to maintain their loyalty as they work from home.

To demonstrate this challenge, let’s review a Chipotle location outside Austin, TX:

Trade Area by Customer Workplace

placeiq location data compares home and work trade area analysis for fast food brand

Trade Area by Customer Residential Dwell

placeiq location data compares home and work trade area analysis for fast food brand
trade areas graphic color key

Each Dynamic Trade Area above represents 60% of it’s closest customers. The difference between the two areas is that the one on the left has been defined using customer workplaces and the one on the right uses customer households. Put simply, most visits to this Chipotle originate from the workplaces immediately surrounding it. If those customers no longer commute into work, this restaurant will have to go much, much further to find the same volume of diners. This data analysis uncovers an actionable local media strategy: they’ll need to invest in an awareness message for a much wider nearby audience.

Pro Tip: To read this visualization, note that the number of customers originating from each region is ranked by the color of the region.  Here, red represents the most and teal represents the least.

This challenging Dynamic Trade Area will not be the case for every Chipotle, however.  It’s important for brands to understand exactly which of their locations will be affected more – or less. Here’s another one just down the road in Texas (San Antonio) that fares much better:

Trade Area by Customer Workplace

placeiq location data compares home and work trade area analysis for fast food brand

Trade Area by Customer Residential Dwell

placeiq location data compares home and work trade area analysis for fast food brand
trade areas graphic color key

This venue is positioned well to adapt to the work-from-home trend. The data shows that it relies on a mixed clientele that visits from home and work. The area only slightly changes when we change the make up. As such, it’s a great venue to support delivery and pick-up business from a customer base cooped up at home.  In this case, Chipotle might consider using media for reminders of their safe preparation and contactless delivery practices.

To take this one step further, we can measure the difference between the work and residential based trade areas for each Chipotle in a market, ranking them by their sensitivity to the “office lunch effect”: larger dots indicate a greater change (higher sensitivity) between the workplace and residential trade areas for that location.

Remote Work Risk Assessment: Chipotle in San Antonio Area

placeiq location data shows risk assessment for fast food brand due to remote working

Using this data, restaurants can better optimize their media investments by Dynamic Trade Area to shift into delivery and pick-up modes that take location-based messaging needs into account: building wider awareness to addressable WFH diners, or merely reminding multi-daypart loyalists of their nearby presence. Another application: competitive restaurants and retailers, especially groceries with prepared food offerings, can identify populations whose access to convenient fast casual has just been removed.

We’ll continue to explore and share ways location data can reveal actionable recommendations and insights in this new environment. You can sign up to receive our weekly newsletter on social distancing trends that dives further into dining, retail, auto, grocery and more. For more information on Dynamic Trade Areas, reach out to your Account Manager or contact our team.