Stop guessing whether marketing drove the store visit — measure it, member by member, against a control group.
Most 'foot-traffic' attribution is inferred from aggregate counters. SocialHub.AI ties a store visit to a known member, deduplicates it, and proves incremental lift with a randomized holdout.
Book a DemoThe Problem in Numbers
Store-visit attribution is usually inferred from aggregate footfall or self-reported scans — no member identity, no control group — so the lift a campaign 'drove' is a guess, not a number a CFO can trust.
A deterministic store visit (a QR check-in or in-store redeem by a known member) fires a member-keyed event, deduplicated at the database with a timezone-aware window. A randomized holdout control group then measures the incremental visits a campaign actually caused.
Lift is computed against a randomized control, not a before/after guess: members are split deterministically into treated and control, control is excluded from the send, and the difference in store-visit rate is the true incremental effect. We never report self-estimated footfall.
How the Loop Solves This
Member-keyed store visits
QR check-in or in-store redeem fires a deterministic, deduplicated store-visit event tied to a known member — the clean signal footfall counters can't give you. (Live.)
Randomized holdout → true lift
A deterministic control group is excluded from the send so incremental store visits are measured against a real baseline, not a before/after guess. (Live.)
Store & region targeting
Reach members by their attributed store or region for location-relevant offers — first-party, no device tracking required. (Region segment rolling out.)
Privacy by construction
Location and push are gated by explicit consent plus the Global Privacy Control signal, fail-closed; one-tap withdraw, no penalty; raw member coordinates are never stored — only derived store-visit events. (Live.)
Proximity re-engagement (near-store)
Re-engage members who came within a set distance of a store within a recent window — geofence-powered via the mobile SDK. The server side (event ingest, near-store/dwell triggers, consent + GPC gates) is built; on-device detection ships with the FlashLocation package (CoreLocation / Play Services geofencing), in active development. Stores only derived enter/dwell events, never continuous GPS.
Frequently Asked Questions
Can I message people who came near a store recently — within a set distance and time window?
The server side of that proximity/geofence audience is built — event ingest, the near-store and dwell triggers, and the consent + GPC gates. The on-device detection ships with the mobile SDK's FlashLocation package (CoreLocation / Play Services geofencing), which is in active development and not yet published or device-validated, so the end-to-end capability is not yet live. Available today is the deterministic store visit — a member who actually checked in or redeemed in-store — which is the cleaner, privacy-safe signal for store-driven campaigns. Proximity always requires explicit location consent and honors the Global Privacy Control signal.
How is this different from generic 'footfall analytics'?
Footfall analytics counts anonymous bodies. This ties a visit to a known member and, with a randomized holdout, isolates the visits your campaign actually caused — a number you can defend to finance, not a directional estimate.
Do members have to share their location?
Not for the live capability: deterministic store visits come from a QR check-in or in-store redeem — no device location needed. The roadmap proximity feature requires explicit location consent (honoring the Global Privacy Control signal, fail-closed) and stores only derived events, never raw coordinates.
Start with deterministic check-ins (QR / redeem) and a holdout on one segment campaign — get a trustworthy lift number first, then widen targeting.
See how SocialHub.AI can deliver these results for your organization.