Industry Trends Data | Macro Trends & Foot Traffic by CBSA, DMA, State, and Nationwide Granularities
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Industry Trends by Placer.ai provides insights on the overall trend across retail industry categories and regions based on foot traffic. The data is useful to understand macro trends, particularly when evaluating the impact and recovery of economic and regional events. For example, COVID-19. Compare foot traffic by category WoW, MoM, and YoY. Track net changes in foot traffic to understand macro industry trends. More granular data sets (all categories, from 2017 onwards) available by request. Please contact us at snowflake_marketplace_sales@placer.ai Featured Industries: Apparel Dining Groceries Home Improvement Hotel/Casinos Electronics Fitness Top Use Cases Conduct industry level market research by DMA, state or nationwide. Benchmark your chain or property against its category, regionally or nationwide. Integrate with internal dashboards. Configuration Options for the full data feed: Data History: Starting January 2018 Time-aggregation options: Weekly Region-aggregation: CBSA, DMA, state or nationwide Delivery frequency: Weekly Delivery methods: Buckets (Amazon - AWS S3; Google - GCS); SFTP Format: CSV Sample Tables & Schema Tables Included: Schema - Industry-Trends Data Schema - Chain Coverage Fields Included: Schema - Industry-Trends Data - region_type - example: ‘DMA’ - region_name - example: ‘DMA, 662 - Abilene-Sweetwater, TX’ - region_code - example: ‘662’ - category - example: ‘Clothing, Electronics store’ - start_date - example: ‘2020-01-06’ - end_date - example: ‘2020-01-12’ - category_foottraffic - example: ‘2528’ - category_foottraffic_previous_year - example:’8973’ - category_group - example: ‘Apparel, Electronics’ Schema - Chain Coverage - id - example: '59ae96a9173f562d3c355488' - name - example: 'Dick's Sporting Goods' - category - example: 'Apparel' - sub_category - example: 'Sporting Goods Shop' - coverage - example: ‘0.9’ - category_group - example: ‘Apparel, Electronics’
