Industry Trends
Industry Trends provide insights into the overall trend of specific retail categories by regions. It is especially useful for evaluating the COVID-19 recovery.
This feed is reflecting the Industry Trends section's data available now in the Placer.ai platform.
Files Delivery
New
Industry trends report can be now be configured to be generated and delivered on a monthly basis.
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Once enabled, files will be uploaded to your cloud bucket (AWS/GCS) or SFTP server (see Delivery Options for further details) once a week or month (based on your configuration).
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Files will be located under this path:
/placer_industry_trends/export/analytics/industry_trends_vm/[weekly/monthly]/
- upload date, example: 2022-03-22 -
Each delivery includes a CSV file for each year (2018, 2019, 2020, 2021) of the industry-trend data for that year. The CSV Schema is detailed below.
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In addition to the industry-trend data, a Chain-Coverage file is provided with each delivery.
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Each file is GZip compressed
Data Retention and Usage
Placer's algorithms and data constantly improve, therefore we include all the history in every export. We strongly suggest not to use data provided in old exports with new ones to avoid data anomalies.
Schema - Industry-Trends Data
Each row represents aggregated trend data of a for a defined aggregation time frame, Region and Category.
Supported aggregation time frames: Weekly and Monthly.
Supported Regions: Nationwide, State, DMA and CBSA (MSA).
Supported Categories such as Clothing, Electronics store, etc. as explained n the Venue Categories.
Categories may be updated
Note that every once in a while Placer may update the categories based on customer feedback. In these cases, feed customer will be given a 2 weeks notice in advance.
| Field | Description | Type | Example |
|---|---|---|---|
| region_type | Included types: 'Nationwide', 'State', 'DMA' & 'CBSA' are supported | String | DMA |
| region_name | Region's name | string | DMA, 662 - Abilene-Sweetwater, TX |
| region_code | Region's code | string | 662 |
| category | Industry Category | string | Clothing, Electronics store |
| start_date | Data start date | YYYY-MM-DD | 2020-01-06 |
| end_date | Data end date (inclusive) | YYYY-MM-DD | 2020-01-12 |
| category_foottraffic | The category's total estimated foot-traffic | number | 2528 |
| category_foottraffic_previous_year | The category's total estimated foot-traffic for the previous year | number | 8973 |
| category_foottraffic_previous_week OR category_foottraffic_previous_month | The category's total estimated foot-traffic for the previous week | number | |
| yoy_weekly_change OR yoy_monthly_change | Year over year weekly change in percenatge | percentage | 0.81 |
| wow_change OR mom_change | Week over week change in percentage | percentage | -0.49 |
| category_group | Business domains | string | Apparel, Electronics |
| industry_id | Identification of the specific industry | string |
Schema - Chain Coverage
Each export folder will include a chain-coverage file, used for:
- Understanding which Chains are included in each Category.
- Evaluating the current venue level coverage (compared to the official number) of each chain.
| Column | Description | Type | Example |
|---|---|---|---|
| id | Placer's Chain ID | string | '59ae96a9173f562d3c355488' |
| name | Chain name | string | 'Dick's Sporting Goods' |
| category | Chain Category | string | 'Apparel' |
| sub_category | Chain Sub-category | string | 'Sporting Goods Shop' |
| coverage | Placer's coverage compare to the official number of venues | percent | 0.9 |
| category_group | Business domains | string | Apparel, Electronics |
Sample
Use the following files to review the delivery format structure.
The actual delivery will include more data (more history/industries/regions).
Link to Sample file
Updated 2 months ago