Foot traffic data for finance by Passby

pass_by's foot_traffic for finance enables Financial Institutions to accurately measure and predict store performance for over a million retail locations across the US. From predictive insights on how much foot traffic a publicly traded retailer will have in the next quarter, to macro analysis on a national brand’s consumers, you can get the data you need to drive your decision-making. Using location data as a base, pass_by’s foot_traffic for finance combines multiple data sources, including ground truth validation from in-store sensors, to not only measure the most accurate footfall data in the market but to predict it 90 days into the future. Using our data feeds, it’s never been easier to enhance your investment models with ground truth-validated retail data.Why pass_by?

1. Ground truth validated: Our store visits data is trained and validated against in-store foot traffic counters, enabling a correlation accuracy of up to 0.91.
2. Largest sample panel: The location data of over 177 million daily US mobile devices are analyzed to power our analytics.
3. Representative panel: This panel of devices closely matches the US national average on age, income, race, and more, making them a representative sample of the population.
4. Proprietary AI models: From clean data to attributing store visits, to producing predictive insights - our AI models are the key to the quality of our datasets.
5. Comprehensive network of datasets: As well as location data, these models are powered by census data, Safegraph’s point-of-interest data, Transunion’s consumer data, and more.
6. Normalized and Scaled: pass_by’s data is scaled to represent the full population, and normalized so as to provide a consistent view of footfall over time.

The Product

-  Foot traffic data for the financial industry. Covering 6,677 brands 1,065 publicly traded)
- Mapped to 484 tickers
- Data starting January 2019
- Point in Time (PIT) starting from March 2022
- 90-day predictive foot counts, with a median 90% correlation to our historical foot traffic

搜集汇总

背景与挑战

背景概述

该数据集提供美国超百万零售点的精准客流监测与预测服务，融合传感器验证与多源数据，具备90天预测能力（准确率90%），覆盖6677个品牌（1065家上市公司）2019年以来的数据。其特色包括0.91的验证准确率、1.77亿设备定位分析及人口统计学代表性样本。