Solution Finder
What is your customer trying to achieve? Pick the problem closest to what they actually said — this translates it into the right methodology, data requirements, and Lab.
You said: “Measure whether advertising drove physical store visits”
Footfall / Store Visit Measurement
MeasurementWhy this fits
Footfall / Store Visit Measurement compares visitation rates between exposed and control populations.
What it answers
Did exposure to a campaign increase the rate at which people visited physical stores, relative to people who weren't exposed?
How it works
Location or exposure data identifies who was shown the campaign, then visitation to defined store locations is measured for both an exposed group and a control group; the difference in visitation rate is the estimated lift.
Data required
- Defined store locations / geofenced polygons
- Exposed population tied to campaign delivery data
- Comparable control population
- Location/visitation event data for both groups
Discovery questions
How are store locations geofenced, and how precise is that boundary?
Imprecise geofences can misattribute nearby foot traffic to the wrong store or miss genuine visits.
How is the control group selected to avoid bias?
If the control group differs systematically from the exposed group (e.g. lives further from stores), the lift estimate will be biased.
What baseline visitation rate exists before the campaign?
Lift is only meaningful relative to an accurate pre-campaign baseline.
Implementation approaches
Risks & limitations
Risks
- Geofence inaccuracy leads to false positive or missed visits
- Confusing correlation ('visited after seeing ad') with causation ('visited because of ad')
Limitations
- Location data coverage varies by device and opt-in rates, which can undercount true visitation
- Even well-designed studies cannot fully rule out confounding factors