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Retail Impact Scoreboard

Comparing current foot traffic to the pre-COVID-19 days can provide a deep understanding of the commercial and economic impact of the virus across different industries and geographies. Better still, industry- or brand-specific traffic trends can help identify early signals of a potential recovery in different regions of the US.

  • Visualize foot traffic trends for a variety of industries in all 50 states

  • Compare visitations in 2020 with visitations in 2019- Connect COVID

  • Connect COVID-19-related news story publications to changes in behavior

Retail Impact Scoreboard screenshot

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Retail Analytics Podcast

PDF cover page - Retail Analytics Podcast

Track which industries are seeing visitation changes as a result of COVID-19

What are you looking to do?

By observing which areas are increasing in population especially with constituents of current prosperous areas, our partners are determining those areas most likely primed for growth and making early investment decisions.

Make adjustments to brand’s supply chains based on geographic changes in demand.

Demonstrate locations’ lack of addressable market due to COVID19 impact to bolster negotiation tactics.

Even if all industries reopen, how will consumers choose to spend their time? Will the visitation breakdown by industry be similar to pre-COVID19 periods? Which industries will gain a greater share of consumers wallets?

  • How many people are visiting my store & what will this look like in the future?
  • Optimize signage or merchandising
  • Benchmark one or more locations’ ability to capture and understand the addressable market
  • Discover patterns in peak visitation, repeat or lapsed visitors, or visitor mix
  • Find out which areas have gaps in the local marketplace
  • Benchmark competitors’ peak visitations, foot traffic, and uncover previously unknown competitors


Learn more on our blog

Have questions about our methodology? Have a look at the articles and data schema below.

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Blog

Unacast Announces Global Partnership with ShopperTrak

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Blog

Nike or Adidas: Which shoe retailer has the better recovery game?

Blog thumbnail image restaurant
Blog

Restaurant Industry Recovery Insights - June 2020

Blog thumbnail image black friday
Blog

Black Friday 2020: Light Foot Traffic Projected

Driven by data science to maximize actionability

Unacast's robust team of data scientists and PhDs focuses on data accuracy to ensure that our partners and clients get high-quality insights that reflect real-world events.

  • Our scoring system is based on the percentage increase or decrease from the same day in 2019:
    Hot: >20% increase
    Warm: 0-20% increase
    Cool: 0-20% decrease
    Cold: >20% decrease

  • We assign speed-analogy labels to the rapidity of industry recovery based on the trend slope:
    Stopped: slope
    Slow: slope between 0 and 0.01
    Fast: slope between 0.01 and 0.03
    Very Fast: slope > 0.03

  • Interested in traffic trends at a more granular level such as neighborhoods, brands, or venues? Contact our team to learn how our data feeds and insights reports can help.

What else can our data do?

The Scoreboard tool above is a high-level demonstration of how our data can help companies understand the current picture and react accordingly. Please contact us if you’d like to dig deeper:

Finer Resolution

Zoom in from the national and state-level maps to get insights at the county, city, neighborhood, census block group, or venue scales.

Industry Details

Our Real World Graph® comprises many more industries than what’s demonstrated above, including sub-categories (such as QSRs), brands, or individual locations.

Visitor Stratification

See traffic fluctuations broken out by different types of visitors, such as local visitors, tourists, or workers.

Contact us to learn more

oRIGINAL Full Schema (NEW ONE TO COME!)

Curious about exactly what data is included in the Retail Impact set? Check out the schema below. To download a sample of the data, please click here.

SCHEMA: Retail Impact Scoreboard at United States State-level data

Field Name
Type
Description
Description
Pro Bono Set
Extended Set
date
DATE
Date of observation
2/2/2020
Checkmark
Checkmark
state_name
STRING
Full text name of the state
Oklahoma
Checkmark
Checkmark
category
STRING
"POI category: [Hospital & Pharmacies, Grocery stores, Gas Stations, Gym and Sports, Eating places, Delivery services, Entertainment] 'Total All Industries' has the sum"
Travel & Hospitality
Checkmark
Checkmark
person_count_per_venue_2020
INTEGER
Average number of daily visits to a venue in given <category>, <state_name> on <date>. Denominator is based on total number of tracked venues.
111
Checkmark
Checkmark
person_count_per_venue_2019
INTEGER
Baseline expressing average number of daily visits in given <category>, <state_name> on closest <date> in 2019 aligned on day of week.
101
Checkmark
Checkmark
diff_perc
FLOAT
Percentage change in visitation compared to last year.
0.09
Checkmark
Checkmark
diff_bucket
STRING
Verbal classification of difference in visitation compared to last year (<diff>).
0-20% increase
Checkmark
Checkmark
longterm_diff_perc
FLOAT
Percentage change in visitation since start of the long-term trend period (since Feb 1). Mean average from first and last 7 days of the period are compared.
0.00
Checkmark
Checkmark
midterm_diff_perc
FLOAT
Percentage change in visitation since start of the mid-term trend period (last 4 weeks). Mean average from first and last 7 days of the period are compared.
-2.50
Checkmark
Checkmark
shortterm_diff_perc
FLOAT
Percentage change in visitation since start of the short-term trend period (last 2 weeks). Mean average from first and last 3 days of the period are compared.
27.72
Checkmark
Checkmark
longterm_diff_perc_trend
FLOAT
Percentage change in estimated visitation since start of the long-term trend period (since Feb 1). Values are based on the first and last value of the trend regression line.
-37.85
Checkmark
Checkmark
midterm_diff_perc_trend
FLOAT
Percentage change in estimated visitation since start of the mid-term trend period (last 4 weeks). Values are based on the first and last value of the trend regression line.
3.16
Checkmark
Checkmark
shortterm_diff_perc_trend
FLOAT
Percentage change in estimated visitation since start of the short-term trend period (last 2 weeks). Values are based on the first and last value of the trend regression line.
14.49
Checkmark
Checkmark
longterm_trend_slope
FLOAT
Slope of regression line (trend line) fitted to data of the long-term period.
-0.37
Checkmark
Checkmark
midterm_trend_slope
FLOAT
Slope of regression line (trend line) fitted to data of the mid-term period.
0.00
Checkmark
Checkmark
shortterm_trend_slope
FLOAT
Slope of regression line (trend line) fitted to data of the short-term period.
0.01
Checkmark
Checkmark
longterm_recovery_speed
STRING
Indicator translating slope of trend into verbal classification of recovery speed.
Stopped
Checkmark
Checkmark
midterm_recovery_speed
STRING
Indicator translating slope of trend into verbal classification of recovery speed.
Slow
Checkmark
Checkmark
shortterm_recovery_speed
STRING
Indicator translating slope of trend into verbal classification of recovery speed.
Fast
Checkmark
Checkmark
longterm_recovery_days
INTEGER
Estimate of number of days til long-term trend line intersects 2019 traffic (smoothed using 7-day rolling average).
-1
Checkmark
Checkmark
midterm_recovery_days
INTEGER
Estimate of number of days til mid-term trend line intersects 2019 traffic (smoothed using 7-day rolling average).
78
Checkmark
Checkmark
shortterm_recovery_days
INTEGER
Estimate of number of days til short-term trend line intersects 2019 traffic (smoothed using 7-day rolling average).
3
Checkmark
Checkmark
momentum
FLOAT
Momentum that expresses strength of the trend on <date> computed by means of stochastic oscillator method over period of last 21 days.
0.13
Checkmark
Checkmark
forecast
BOOLEAN
Binary flag expressing whether values are based on observation or prediction.
FALSE
Checkmark
Checkmark
insured_unemployment_rate
FLOAT
Last recorded rate of insured unemployment in <state>.
3.32
Checkmark
Checkmark
Expand table

Additional COVID-19 LOCATION DATA TOOLS

Social Distancing Scoreboard

A browser-based interactive tool that measures COVID-19- driven changes in human mobility in any US county (updated daily)

Try the Scoreboard

Migration Patterns & Emerging Areas

Measure changes in the human mobility patterns of larger areas, such as movement within neighborhoods or between states.

Try the Scoreboard

What can human mobility insights do to combat COVID-19?

Book a meeting below or contact us at talkToUs@unacast.com and one of our experts will contact you shortly.

  • 1

    Get in touch to discover how location data can help your organization measure the impact of the virus.

  • 2

    Chat with our team to determine data strategies to tackle your particular challenges.

  • 3

    Use high-quality, curated data and human mobility insights to create reaction strategies and identify recovery signals.