Introducing: Unacast’s Retail Impact Scoreboard

Introducing: Unacast’s Retail Impact Scoreboard

The data powering the first tool in our COVID-19 Location Data Toolkit helped government leaders and health experts underscore the importance of social distancing. And while we believe that social distancing has led to a reduction in COVID-related deaths, it’s no surprise that the dramatic reduction in human mobility is putting a dent in our economy, and in particular, the retail industry.

That’s why we’re introducing our next tool: the Retail Impact Scoreboard.

By comparing current foot traffic to pre-outbreak foot traffic, we can provide retailers, retail analytics groups, and other purveyors of goods and services with a better understanding of the virus’ commercial impact across a variety of geographies and retail types.

There are many more ways to slice, dice, and visualize this data — so we’ve created an easily-digestible dashboard to help companies monitor which industries are “heating up” or “cooling down” without having to wrangle with the data.

After looking at 2020 versus 2019 traffic trends and the distribution of the changes to last year, we noticed where the natural breaks occurred. Accordingly, we use a temperature analogy to bucket the differences between last year and this year into the following strata:

  • Hot: "More than 20% increase in traffic trends from 2019
  • Warm: "Between 0% and 20% increase in traffic trends from 2019
  • Cool: "Between 20% and 0% decrease in traffic trends from 2019
  • Cold: More than 20% decrease in traffic trends from 2019

Industry-Level Insights

For a high-level view of one kind of retailer, we can choose a single category and get a broad-brush picture of every store or brand that falls into it. Let’s take grocery stores as an example.

When the US declared a national state of emergency, we saw a peak grocery store foot traffic:

Scared and uncertain of the future, shoppers engaged in “panic buying” before settling into a significant reduction from the average pre-outbreak visitation. Interestingly, when we looked at dwell time for grocery stores, we saw the inverse curve:

In other words, people across the country shopped for groceries less frequently, but they also spent more time doing so. Why? We theorize that it’s likely to be a combination of behaviors:

  • Stocking up: knowing ahead of time that they will be visiting less frequently, shoppers collect and purchase more food and goods to carry them through to their next visit
  • Capacity reductions: many grocery and food retailers are limiting the number of customers that can be in the store at the same time, creating queues outside the store that increase wait times
  • Social distancing within stores: some retailers also created physical mechanisms so that shoppers could remain the recommended six feet away from each other, creating longer lines within the store and increasing wait times
  • Staff reductions: some retailers are protecting their workforce by having fewer employees on the floor to help customers find items and make purchases

Factoring in other data sets (such as point-of-sale data) on a store-by-store basis, or aggregating to the brand or regional level, could help confirm to what extent these are true. In any case, we can conclude that in general, people are largely following recommended policies, and shopping smarter rather than continuing to “panic-buy”:

We know that industries do not perform uniformly across geographies. In the same vein, we expect that certain geographies will recover in certain industries sooner or later than others. Uptick in activity for some areas can be harbingers for an overall recovery for a given industry. 

We highly encourage users to engage with the new Scoreboard and send us feedback about what’s working and what insights your organization still needs to see. In the coming days and weeks we’ll be adding features and making improvements with the ultimate aspiration of providing the retail industry — who employ many millions of people — with the right information at the right time, allowing them to shore up their businesses and keep their staffs, and their communities, strong.

But we’re just getting started.

We’ll know that our Retail Impact Scoreboard is succeeding if it helps businesses and policy makers manage current challenges and forecast recovery plans.

But this new interactive tool is only a high-level demonstration of what our data science team, in combination with our award-winning data engine, the Real World Graph®, can do for retailers and retail-adjacent organizations. Not only can we provide points of view for physical places as small as a city block and as large as the whole US, but we can also look at location data at three different retail-specific levels:

  • Industry, as demonstrated above
  • Brand
  • Venue

Brand-Level Insights

We can also view our insights through the lens of different retail brands. We anticipate that this could be very useful to uncover national trends or regional differences in performance.

For example, home improvement retail is widely considered an essential business, so we looked at Lowe’s in California, where state and local leaders enacted strict measures prior to the national declaration of emergency, and in Texas, where reaction was later and the measures less strict:


Looking at 2020, visitations follow the same pattern in these two large and populous states until around the second week of March, where they begin to diverge.

As states / regions around the country begin reopening at different times, we expect such divergences to continue, but also to evolve. Most recently, the chart shows that as California reaches the crest of its virus curve but Texas is still on the incline, the divergence could become a mirror image for a period of time. Brands and retailers can use insights like this to understand how to optimize their supply chain and operations accordingly, while retail real estate operators can better predict from where they can begin recouping rent losses.

Venue-Level Insights

For both individual venues and the neighborhoods that surround them, we can further refine the point of view.

For example, incorporating our Home and Work data sets enables us to stratify types of visitors to get different types of insights, as we did when we looked at this Volkswagen assembly plant in Chattanooga, TN:

When we focus only on workers, we see a downward trend in visitation as the plant begins to close down. We presume that a reverse trend preceding a reopening will be an early recovery indicator for retailers like dealerships, gas stations, insurance companies, and other automobile-adjacent providers of goods and services.

Interested in more? We are here to help!

The three examples above are a small window into what kinds of knowledge retailers can glean from Unacast’s location data. A few others include:

  • Competitive Intelligence: how post-outbreak visitation patterns of competitive brands.
  • Cross-visitations: which other brands and store types your customers frequent, and how that’s changed since the outbreak
  • Redefined Local Trade Area: how your trade area is being redefined in terms of distances customers will travel to get to your store, and how that changes marketplace understanding
  • Targeted Recovery Indicators: Shows which retail categories are seeing an uptick in visitations when recovery begins.

Depending on what works for your organization, we offer a variety of deliverable types:

  • Data feeds: access our Venue or Neighborhood Packages via API or importable files like JSON or CSV.
  • Report Studio: Get a customized report that visualizes the activity in the venue or neighborhood of your choice.
  • Insights Dashboard (coming soon!): Visualize insights on a browser-based interface using a unique login.

Moreover, when you combine Unacast’s location data with other data sets in your tech stack, you will get an even fuller picture of the present moment and a better idea of what the future holds. For example, marrying sales and foot traffic data can uncover potential growth opportunities, such as seeing which products are underperforming or overperforming to aid inventory planning. Our Neighborhood Data Package can provide even earlier indicators. By understanding the overall return in human movement to a given area, we can derive precursors for potential industry-specific recovery.

Contact us to find the right solution for your needs. 

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