Here is a number most retail executives have never seen for their own stores: the ratio between people who walk past the entrance and people who walk in. Not because it is hard to calculate — it is a simple division — but because most chains only measure one half of it. They count footfall traffic inside the door and take the landlord's word for what happens outside it. That gap is where money leaks: in rent negotiations argued without evidence, in window displays judged on gut feel, and in three separate hardware budgets doing the work of one.
Most estates that measure footfall, passerby traffic and demographics do it with three vendors, three devices, three cabling runs and three support contracts. The purchase price is the visible cost. The invisible one is operational: three firmware update cycles, three points of failure above every entrance, and three data schemas that a BI team has to stitch together before anyone can answer a question as basic as "what share of Saturday afternoon passersby are we converting, and who are they?"
A single Vemco sensor collapses that stack. One device captures footfall (how many people actually enter), passerby traffic (how many walk past the front of the store — along the mall corridor or the street outside the door) and anonymous customer demographics. One installation, one power feed, one platform. For a 200-store chain, the difference is not marginal; it is the difference between one installation programme and three, and between one data model and a reconciliation project.
Every store manager asks the same question: how many people really pass in front of my store? Landlords and mall operators publish traffic figures, but those figures are theirs — aggregated, often modelled, and rarely specific to your unit's frontage. An independently measured passerby count changes the dynamic. When your lease comes up and the landlord cites corridor traffic to justify an increase, you can respond with your own measured data for your own doorway, hour by hour, season by season.
Pair the passerby count with footfall and you get capture rate — the percentage of passing traffic that enters. This is the metric that separates a location problem from a store problem. If 8,000 people pass daily and 4% enter, the storefront, window merchandising or entrance layout is underperforming, and that is fixable. If only 1,500 pass, no amount of visual merchandising rescues the site, and the conversation belongs in the property portfolio review, not the marketing meeting. Chains that track capture rate consistently tend to discover that their internal "top store" rankings are really rankings of landlord-delivered traffic, not store performance — which changes who gets promoted and which formats get rolled out.
People counting only creates value at chain level if store A's number means the same thing as store B's. Mixed-vendor estates fail this test constantly: one device counts children, another does not; one double-counts staff at the fitting rooms, another excludes them. The result is a league table nobody trusts, and once regional managers stop trusting the data, they stop acting on it.
Vemco's counting is standardized across every sensor and every location, with a contractual minimum accuracy of 96% — and typically 98–99% in practice, where lighting, store layout and visitor behaviour allow. The contractual floor matters more than the headline figure: it means the accuracy claim survives a difficult entrance, a dark corridor, or a doorway where shoppers linger. Ask any vendor promising a flat 99% what happens at a wide mall entrance with heavy pram traffic and glass reflections; the honest ones will talk about conditions. The dishonest ones will change the subject.
Demographic detection is where many analytics projects die in legal review. Vemco's approach is fully anonymous with no personal data stored, which keeps it GDPR-friendly and — just as importantly — keeps your data protection officer out of a six-month impact-assessment cycle. What you get is aggregate patterns: the audience walking past versus the audience walking in, by daypart. If the corridor skews one demographic in the evening and your entering traffic skews another, your window, your promotions or your assortment are speaking to the wrong people at the wrong hour. That is a merchandising decision you can test within a week, not a quarter.
A field observation from rollouts: the single biggest cause of delay in multi-device deployments is not the technology — it is ceiling access and landlord permissions. Every device above a shopfront needs sign-off from centre management, and every cable run through a shared ceiling void triggers a contractor visit and, often, an out-of-hours work permit. Cutting three boxes to one does not just cut hardware cost; it cuts the number of permissions, penetrations and night shifts by two-thirds. On a large estate, that is frequently the difference between finishing a rollout before peak season and finishing after it.
The device is only half the argument. All three data streams flow into the same platform as Vemco's footfall and tenant analytics — alongside VemCount and VemTenant — over an open API. That matters for two reasons:
For executives weighing this decision, the evaluation question is simple: price the three-system alternative honestly — hardware, cabling, installation, permissions, support contracts and integration labour — then compare it to one sensor per entrance feeding one platform. In most estates, the single-device case wins before you even count the strategic upside of independent passerby data at the negotiating table.
If your next lease renewal, capture-rate review or analytics consolidation is on the horizon, it is worth seeing what one sensor can replace in your estate. Talk to the Vemco Group team about a passerby and footfall pilot at your highest-rent locations at vemcogroup.com/contact-us — and bring your landlord's traffic figures with you.