Chain/Venue Multi-Metrics

Once enabled, Placer will upload to your bucket CSVs in the schema detailed below on a daily/weekly basis.
Each row in the CSV will be provided in the Schema format detailed below.

Filenames, path and retention policy:

  1. Files will be provided on Amazon Web Services (AWS) S3 or Google Cloud Storage (GCS) (see Delivery Options)

  2. Files will be exported until midnight UTC every day.

  3. File path will include your custom bucket name with the following addition: '/export/analytics/multi_metrics/[DAILY/WEEKLY]/[DATE]
    Daily/Weekly- refers do data aggregation type by time

  4. Files are divided per chain and export configuration. Examples:
    multi_metrics_daily_chain_2020-07-27_Walmart_2017-01-01_2020-07-25.csv.gz - Walmart Daily aggregated chain data, that was published on Juy 27 2020. File includes historical data from January 2017.
    multi_metrics_weekly_property_2020-07-27_Walmart_2017-01-01_2020-07-25.csv.gz - Walmart Weekly individual property level Data, that was published on Juy 27 2020. File includes historical data from Januray 2017.

  5. Files are GZip compressed

CSV Schema Overview

ColumnDescriptionTypeExample
publication_dateData publication dateISO-86012020-08-01
version_codeCalibration version codestring1.0.0
idPlacer's entity IDstring'5965fca0173f564b883c222e'
nameEntity namestring'Mcdonald's'
typeEntity type. See Location Info for a full list of supported types.string'venue'
time_frameData aggregation resolution. Supported values: 'daily', 'weekly' and 'monthly'.string'daily'
start_dateData start date.ISO-86012018-04-15
end_dateData end date (inclusive)ISO-86012018-04-15
region_type'nationwide', 'state', 'dma' & 'cbsa' are supported.
Only available for entities of type 'chain.'
Note: CBSA is the union of MSAs & micro-MSAs
string'nationwide'
region_nameRegion's namestring'California'
region_codeRegion's codestring'CA'
latThe entity's latitude.
Only available for entities of type 'venue' or 'complex
double37.786099
lngThe entity's longitude.
Only available for entities of type 'venue' or 'complex
double-121.46352
ticker_symbolThe stock exchange ticker symbolstring'MCD'
company_nameIf available, the parent company chain name.string'Mcdonald's'
foottrafficEstimated foot-traffic in the location during the relevant time frame. Foot-traffic is extrapolated from the panel_visits.integer5000000
panel_visitsThe actual number of visits generated by Placer's panel (observed visit).integer50000
home_distance
_estimated_foottraffic_X
(multiple columns)
The accumulative foottraffic generated by customer who live within 'X' miles of the property.
Includes a column per distance(mi) as follows: 0.3, 0.5, 0.7, 1, 2, 3, 5, 7, 10, 30, 50, 100, 250, 250+
Example: 'home_distance
_estimated_foottraffic_50' means the sum of foottraffic generated by customers who live within 50 miles of the property
integer40000
home_distance
_percentage_X
(multiple columns)
The percentage of foottraffic generated by customer who live within 'X' miles of the property.
Includes a column per distance(mi) as follows: 0.3, 0.5, 0.7, 1, 2, 3, 5, 7, 10, 30, 50, 100, 250, 250+
double25.485
work_distance
_estimated_foottraffic_X
(multiple columns)
The accumulative foottraffic generated by customer who work within 'X' miles of the property.
Includes a column per distance(mi) as follows: 0.3, 0.5, 0.7, 1, 2, 3, 5, 7, 10, 30, 50, 100, 250, 250+
integer4000
work_distance
_percentage_X
(multiple columns)
The percentage of foottraffic generated by customer who work within 'X' miles of the property.
Includes a column per distance(mi) as follows: 0.3, 0.5, 0.7, 1, 2, 3, 5, 7, 10, 30, 50, 100, 250, 250+
double13.35
foottraffic_per_sqftThe number of visits at the property during the relevant time frame by its size (square footage)double10.91

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Additional Metrics

From time to time Placer may add additional metrics to the schema. They will be appended as additional columns and you will be given a 30 days note in advance with a sample file.

Sample file

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Note

  • Use the following files to review the delivery format structure and compression method.
    • The actual delivery will include more data (more history/chains/venues).
    • Files contain fake-data and therefore cannot be used for data-evaluation

Sample #1 - Daily Chain-level data for Walmart Supercenter, Only January 2017

Sample #2 - Weekly Venue-level data for a selected Applebee's store in Q1/Q2 2017