Top Foot Traffic Data Providers in 2026

People searching for foot traffic data providers usually want names, so this guide starts with nine. Each provider is described from its own public pages, a partner's announcement or, where labelled, a marketplace listing the provider wrote, and each sourced fact links to where it came from. GSDSI sells mobility and points-of-interest data, so read this as a vendor's guide: neither the list nor its order is a ranking. The useful split is what you receive. Some providers sell a dashboard with modelled visit estimates. Some sell visit counts by place as files. Some sell raw, device-level location data that your own team turns into visits. One well-known name, SafeGraph, sold its Patterns business to Advan in 2022 and now leads with places data. For how visit counts are built and where they go wrong, start with how accurate foot traffic data is.

Top foot traffic data providers in 2026, compared

Each row below uses the provider's own public pages, a partner's announcement or a seller-written marketplace listing, labelled as such. Scale is shown exactly as each provider states it. Where we did not find something, the cell says "Not found on the pages we checked" rather than guessing. GSDSI appears as one option, described only from what our own Global Mobility & Location Data page publishes.

Read the scale column with care. Foursquare counts visits per year, Unacast counts daily signals and monthly devices, and Advan counts tickers. Unacast's figure comes from a profile the seller wrote. None can be ranked against the others. Panel size is only part of it: how many devices you need for a read depends on the store and the question.

Three kinds of foot traffic provider, and why it matters

• Software and analytics. Placer.ai sells a foot traffic platform with exports and an API, and Pass_by says it helps you understand what drives store performance. On the pages we checked, we did not find raw device-level data offered, so you inherit their modelling.
• Visit data and attribution. Advan and Unacast sell visits already matched to places, and Foursquare sells visit attribution. Good for trend work and attribution without a data science team.
• Location data feeds. Unacast and Veraset offer location data by API or bulk file, and GSDSI licenses device-level events your team matches to places itself. Most flexible, and it puts the privacy and sensitive-location duties on you.

SafeGraph sits outside all three now: it sold its Patterns business to Advan and leads with places data. GSDSI is in the raw-data group as a licensor, and pairs its mobility feed with a points-of-interest file of 23M+ U.S. POIs with polygon boundaries and coverage in 150+ countries, so visits can be attributed to places in your own warehouse. See POI and geofencing data and the POI data overview.

How to choose a foot traffic data provider by job to be done

• Site selection and store performance, without a data team: Placer.ai or Pass_by.
• Benchmarking visits against competitors: Advan or Placer.ai for ready trends, or a raw feed matched to your own store list. See competitive benchmarking.
• Revenue proxies for investing: Advan's ticker-mapped data, or raw data your quants model themselves. See alternative data for finance.
• Media attribution to store visits: Foursquare's attribution product.
• Building your own visit model: Veraset, Unacast or GSDSI, joined to a points-of-interest file with polygons.
• Visit monitoring from a mobile panel: Huq.

Why two providers give different visit counts for the same store

Every provider runs the same three steps: collect device signals, match them to places, and extrapolate from the panel to the population. Each step can move the number. Panels differ in which apps feed them and how many devices they see in a given town. Matching depends on the place data: a centroid pin and a polygon assign the same ping to different stores, which is why POI data quality decides foot traffic accuracy. Extrapolation weights the panel by age, geography and platform, and providers use different weights. Direction of change is usually easier to compare across providers than level, so judge providers on how well they track a ground truth you hold.

Methodology and sources

GSDSI sells mobility and points-of-interest data, so we have an interest in this category; the table is a list, not a ranking. The facts were compiled on October 2, 2026 from each provider's own public pages, partner announcements, and, where labelled, marketplace listings written by the seller. Each fact links to the page it came from. Figures are shown as each provider states them and are not converted or averaged. Where a provider's own pages disagreed, we left the figure out. "Not found on the pages we checked" means exactly that, not that the provider lacks it. The "Best fit" column is our opinion. Providers can email corrections to info@gsdsi.com, and we will update the row with a link to the new source.

The legal context for foot traffic data, as of October 2026

Foot traffic data starts as device location, and the FTC has drawn a clear line around sensitive places. In January 2024 the FTC announced an order against X-Mode Social and Outlogic and a proposed order against InMarket. In January 2025 it finalized an order against Gravy Analytics and Venntel that prohibits selling, disclosing or using sensitive location data except in limited national security or law enforcement cases. In June 2026 a federal judge in Idaho signed an order in the FTC's case against Kochava restricting how it sells, shares or discloses sensitive location data. Some state privacy laws add their own limits on selling precise location.

For a buyer, the practical questions are which places a provider excludes, at what aggregation level visits are reported, how consent was captured, and whether you are allowed to hold device-level data at all for your use. Put the answers in the contract; our guide to data licensing agreement red flags lists the clauses. This section is general information, not legal advice; take specific questions to counsel.

What to ask every foot traffic data provider

Test design: check against ground truth before you sign

1. Pick 20 to 50 places where you hold ground truth, such as door counts, transactions or sales, over at least six months.
2. Ask every finalist for visits to the same places over the same weeks, using your own place list or polygons.
3. Score how well each provider tracks week-to-week change, not just the level, and note where it breaks down.
4. Check coverage in your smallest markets, where thin panels show up first.
5. Write the scoring weights down before the files arrive, as described in seed-match testing.

Where GSDSI fits

GSDSI licenses device-level mobility data from consented app, Wi-Fi and SDK sources, with a daily U.S. and Canada feed, country-specific feeds and history back to 2019. Location events are matched to our points-of-interest file for visit attribution, and the data is delivered as CSV, JSON, Parquet or API. It is raw data for your own warehouse, not a dashboard. If that is what you need, request a sample through the Global Mobility & Location Data page and run it through the same ground-truth test as every other finalist.