Industry Trends Data | Macro Trends & Foot Traffic by CBSA, DMA, State, and Nationwide Granularities

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 [e-mail]

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’

搜集汇总

背景与挑战

背景概述

该数据集通过客流量分析零售行业的宏观趋势，覆盖自2018年以来的周度数据，支持CBSA、DMA、州和全国等多级区域粒度。它包含行业类别和连锁覆盖的详细字段，适用于市场研究和基准比较。