Foot traffic attribution
Foot traffic (store visit) attribution estimates how many people visited a physical location after seeing or clicking an ad, using aggregated and modeled location signals from consenting panels rather than tracking individuals. Because it relies on sampling and modeling, it is reported as a modeled estimate above a privacy threshold, not a precise headcount. It bridges digital ads to offline visits where on-site conversion tracking cannot reach.
How it is estimated
Platforms use aggregated location signals from a panel of users who consented to location history, then model the full population from that sample. They compare ad-exposed users' subsequent visit rates to a baseline to estimate incremental visits.
The result is a modeled number reported only when it clears a minimum threshold, to protect the privacy of small groups.
Reading it responsibly
Because the figure is extrapolated from a sample and modeled, treat it as directional, not exact. It is most useful for relative comparisons — which campaigns or locations drove more incremental visits — rather than precise ROI per visit.
It should never be presented as individual tracking, and small-segment estimates are deliberately withheld.
- Built from consented, panel-based location samples
- Population-modeled and threshold-suppressed
- Directional estimate, not a literal visitor count
How it appears in analytics and logs
A modeled store-visit estimate indicates directional offline impact from ads; it is an extrapolation from a panel, not a literal count of every visitor.
Diagnostic use case
Gauge whether a local or omnichannel campaign drove physical store visits, for businesses whose conversions happen offline.
What WebmasterID can help detect
WebmasterID measures online behavior only; foot-traffic estimates sit beside its first-party web data to complete an online-to-offline picture.
Common mistakes
- Reading a modeled estimate as an exact visitor count.
- Implying individual location tracking is involved.
- Trusting estimates for tiny segments below thresholds.
Privacy and accuracy notes
Estimates come from aggregated, consented, modeled location data and are suppressed below thresholds; no individual visit is tracked. Educational, not legal advice.
Related pages
- Store visit conversions
Store visit conversions are an ad-platform measurement that estimates how many people visited a physical store after seeing or clicking an ad. Google documents that store visits are modeled and aggregated, derived from anonymized, consented location data and statistical extrapolation rather than tracking specific individuals into a shop. This page explains the modeled nature of the metric and how to read it responsibly.
- TV and offline attribution
TV, radio, and print have no click, so their attribution is built from indirect evidence: correlating exact spot airtimes with spikes in site traffic and search, dedicated vanity URLs and promo codes, self-reported surveys, and — most rigorously — geo or matched-market experiments that compare regions with and without the buy. Each method trades precision for the reach these channels uniquely deliver.
- Modeled conversions
Modeled conversions are conversions a platform estimates statistically rather than observes directly. When direct measurement is blocked — by missing consent, cross-device journeys, or privacy protections — ad and analytics platforms model the likely conversions from observable trends and aggregated data, and report them alongside observed ones. Understanding which conversions are modeled is essential to reading attribution honestly.
- Privacy-first analytics
Aggregate, privacy-safe online measurement.
Sources and verification notes
- Google Ads Help — About store visit conversionsDocuments modeled, threshold-limited store-visit estimation.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.