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.

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

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.

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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.

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

date

DATE

Date of observation

2/2/2020

state_name

STRING

Full text name of the state

Oklahoma

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

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

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

diff_perc

FLOAT

Percentage change in visitation compared to last year.

0.09

diff_bucket

STRING

Verbal classification of difference in visitation compared to last year (<diff>).

0-20% increase

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

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

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

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

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

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

longterm_trend_slope

FLOAT

Slope of regression line (trend line) fitted to data of the long-term period.

-0.37

midterm_trend_slope

FLOAT

Slope of regression line (trend line) fitted to data of the mid-term period.

0.00

shortterm_trend_slope

FLOAT

Slope of regression line (trend line) fitted to data of the short-term period.

0.01

longterm_recovery_speed

STRING

Indicator translating slope of trend into verbal classification of recovery speed.

Stopped

midterm_recovery_speed

STRING

Indicator translating slope of trend into verbal classification of recovery speed.

Slow

shortterm_recovery_speed

STRING

Indicator translating slope of trend into verbal classification of recovery speed.

Fast

longterm_recovery_days

INTEGER

Estimate of number of days til long-term trend line intersects 2019 traffic (smoothed using 7-day rolling average).

-1

midterm_recovery_days

INTEGER

Estimate of number of days til mid-term trend line intersects 2019 traffic (smoothed using 7-day rolling average).

shortterm_recovery_days

INTEGER

Estimate of number of days til short-term trend line intersects 2019 traffic (smoothed using 7-day rolling average).

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

forecast

BOOLEAN

Binary flag expressing whether values are based on observation or prediction.

FALSE

insured_unemployment_rate

FLOAT

Last recorded rate of insured unemployment in <state>.

3.32

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What are you looking for?

Retail Impact Scoreboard

Trusted and featured by

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.

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Driven by data science to maximize actionability