Foot Traffic Patterns Data | United States Retail

Chain/Venue Multi-Metrics by Placer.ai is a data feed of foot traffic visitation for any property or retail chain in the U.S. Leverage historical foot traffic data and analyze trends to forecast future retail visitation performance for any leading chain and individual venue you are interested in.

Track accumulative foot traffic, as well as percentage of foot traffic, generated by customers who work and live within a certain mileage of the property.

More granular data sets (all categories, from 2017 onwards) available by request. Please contact us at [e-mail]

Featured Metrics
- Total foot-traffic: Number of visits in a given time period.
- Visitors breakdown by home distance: a dimension that breakdowns foot-traffic data by the distance of the home location from the trade area.
- Visitors breakdown by work distance: a dimension that breakdowns foot-traffic data by the distance of the work location from the trade area.
- Panel size: Number of panels (devices) in a given time period
Top Use Cases
- Monitor stores and properties to track high and lower performers
- Integrate visit traffic data with prediction models to forecast future performance and strategically plan marketing promotions and hiring needs
- Validate or identify anomalies in other datasets (e.g. credit cards)
- Analyze the impact of events and promotions on vist traffic
- Integrate with internal dashboards for reporting and a day-by-day view of chain/venue visitor traffic
- Benchmark store performance within the chain and with category competitors

Configuration Options
- Data history: Starting January 2017
- Time-aggregation options: Daily (only Chains) / weekly (Venues/Chains) / monthly (Venues/Chains)
- Region-aggregation: Nationwide, state, CBSA (MSA+microMSA), DMA (applicable only for Chains)
- Delivery frequency: Daily (only Chains) / weekly (Venues/Chains)
- Delivery methods: Buckets; SFTP or email
- Format: CSV

Sample Tables & Schema
Tables Included:

Fields Included:

- publication_date - example: ‘27/07/2020’
- version_code - example: ‘0.0.3’
- id - example: ‘58ef6e6c173f5601f82d8f28’
- name - example: ‘Walmart’
- type - example: ‘chain’
- time_frame - example: 'daily'
- start_date - example: ‘01/01/2017’
- end_date - example: ‘01/01/2017’
- region_type - example: ‘state’
- region_name - example: ‘Delaware’
- region_code - example: 'DE'

- publication_date - example: ‘2020-08-0’
- version_code - example: ‘1.0.0’
- id - example: '5965fca0173f564b883c222e'
- name - example: 'Mcdonald's'
- type - example: 'venue'
- time_frame - example: 'daily'
- start_date - example: ‘2018-04-15’
- end_date - example: ‘2018-04-15’
- region_type - example: 'nationwide'
- region_name - example: ‘California’
- region_code - example: 'CA'

For more see, documentation: https://docs.placer.ai/docs/csv-multiple-metrics-export-schema

搜集汇总

背景与挑战

背景概述

Placer.ai提供的美国零售业客流量数据集涵盖2017年至今的全国范围数据，包含总客流量、居住/工作距离分布等核心指标，支持按日/周/月不同时间维度和州/CBSA/DMA等区域维度进行分析，适用于门店绩效监控、营销策略制定等商业场景。数据可通过CSV格式定期交付。