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.
How store visits are estimated
Google documents that store visit conversions are modeled: they extrapolate from a sample of users who have turned on location history and consented to its use, then statistically estimate the total visits attributable to ad interactions. The reported number is an aggregate, not a list of people.
Because it is modeled, store visits are subject to thresholds — Google only reports them when it has enough data to make a reliable estimate.
Reading the metric honestly
Store visits answer a question online conversions cannot: did the ad drive footfall? But the estimate is only defensible in aggregate and over volume. Slicing it too finely, or treating it as a precise count, overstates what the modeling supports.
Reconcile the direction of the trend against independent signals — in-store sales, loyalty scans, or survey data — rather than relying on the modeled figure alone.
- Modeled and aggregated, not individual tracking
- Derived from consented, anonymized location samples
- Reported only above reliability thresholds
How it appears in analytics and logs
A store-visit figure is an aggregate estimate, not a count of identified shoppers; treat it as directional and meaningful only at sufficient volume.
Diagnostic use case
Measure the offline, in-store impact of online advertising for businesses with physical locations, where the conversion happens off the website entirely.
What WebmasterID can help detect
WebmasterID measures only on-site behavior; store-visit estimates come from ad platforms. Use WebmasterID's observed web events as the online half of an online-to-offline picture.
Common mistakes
- Treating a modeled store-visit estimate as an exact headcount.
- Segmenting store visits below the reliability threshold.
- Assuming the metric tracks identifiable individuals.
Privacy and accuracy notes
Store visits are reported as aggregated, anonymized, modeled estimates from consented location signals — not individual tracking. This is educational, not legal advice.
Related pages
- 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.
- Phone call conversions
Phone call conversions count phone calls as conversions and attribute them to the ad or campaign that drove them. Ad platforms use call extensions with dynamic forwarding numbers, or track clicks on a phone-number link on the website, to connect a call back to its source. This page explains how call tracking works, what it can attribute, and the privacy considerations around recording or measuring calls.
- Offline conversion import
Offline conversion import (OCI) connects events that happen away from the website — a sales call that closes, an in-store purchase, a qualified lead in a CRM — back to the online ad click that began the journey. It works by capturing a click identifier (such as Google's GCLID) at the start and later uploading the offline outcome keyed to that identifier, closing the online-to-offline attribution loop.
- Privacy-first analytics
Observed on-site events as the online half of the picture.
Sources and verification notes
- Google Ads Help — About store visit conversionsDocuments store visits as modeled, aggregated estimates from consented location data.
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.