Vemco and Lightspeed: POS Sales and Footfall United in One Analytics Platform

A Saturday with strong revenue can still be your worst-performing day of the week. If 2,400 people walked in and 180 bought, while a quiet Tuesday saw 600 visitors and 90 buyers, your Saturday conversion rate was half your Tuesday rate — and your POS report will never tell you that. Lightspeed knows exactly what you sold. It has no idea who walked past the till without a bag. That missing half of the equation is precisely what a Lightspeed people counter integration solves, and it changes which questions your Monday morning trading meeting can actually answer.

What the integration actually does

The Vemco and Lightspeed integration imports your sales data from Lightspeed directly into the Vemco platform, where it is automatically matched against footfall from your people counting sensors. No CSV exports, no analyst spending Friday afternoons reconciling transaction timestamps against visitor counts in a spreadsheet, no arguments about whether the numbers line up. Once connected, the metrics that matter are computed and displayed inside one platform:

• Conversion rate — transactions divided by visitors, per store, per day, per hour
• Revenue per visitor — the single fairest measure of how well a store monetises its traffic
• Average transaction value against traffic — so you can see whether busy periods dilute basket size
• Store performance comparisons — across locations and time periods, on a per-visitor basis

If you already run people counters from another vendor, that is not a blocker. Vemco is sensor-agnostic by design — Lightspeed sales data can be combined with footfall from any sensor brand, plus IoT and other data sources, in the same analytics environment. You are not locked into a hardware ecosystem to get the POS-plus-traffic view.

Traffic problem or conversion problem? Stop guessing

Every operations manager has had the conversation: revenue is down at a location, and the store manager says footfall was weak. Without visitor data joined to sales data, that claim is unfalsifiable. With both in one platform, the diagnosis takes thirty seconds. If traffic held steady and conversion dropped, the issue is inside the store — staffing levels, stock availability, queue length, merchandising. If traffic itself fell, the problem sits upstream: marketing, local events, weather, a competitor opening nearby. These two failure modes demand completely different responses and completely different budgets. Treating a conversion problem with a marketing spend increase is how retailers burn money twice.

Fair store comparisons — measured per visitor, not per till

Ranking stores by revenue rewards location, not execution. A flagship on a high-traffic street will always out-earn a suburban unit, regardless of how well either team performs. Measure revenue per visitor instead, and the picture frequently inverts: the modest store converting 22% of a small footfall stream may be your best-run operation, while the flagship coasts on volume. For chains, this reframing matters for bonus structures, for deciding which store manager's playbook to replicate, and for having honest conversations with underperformers who can no longer hide behind a busy postcode.

The peak-hour gap: where sales lag behind traffic

Overlay hourly Lightspeed transactions on hourly footfall and a pattern shows up in most stores: one or two windows each day where traffic peaks but conversion sags. Shoppers came in and left empty-handed, often because there were not enough staff on the floor, fitting room queues built up, or the till line looked long enough to abandon. This is the strategic point worth internalising: your POS already knows what you sold — connecting it with footfall tells you what you could have sold. That gap between visitors and transactions is where retail improvement actually lives, and it is invisible to anyone looking at sales data alone. Once the gap is visible per hour, the fix is usually operational and cheap: shifting a lunch break, moving one team member from stockroom to floor between 12:00 and 14:00, opening a second till at a defined traffic threshold.

A note from the field: data quality decides everything

Here is something anyone who has actually rolled this out will tell you: the analytics are only as trustworthy as the count. If store teams discover the sensor tallies staff walking in and out, delivery drivers, or children counted as adults, they will dismiss the conversion figures within a week — and once floor trust is gone, no dashboard wins it back. This is why counting accuracy is not a footnote. Vemco contractually guarantees a minimum of 96% counting accuracy, and in practice achieves 98–99% when conditions such as lighting, store layout and visitor behaviour allow. That distinction — a contractual floor versus a typical result — is worth probing with any vendor you evaluate. A supplier who quotes a flat "99% accurate" with no conditions attached has usually not been asked hard questions. The other practical lesson: exclude staff entrances from counting zones during setup, and validate counts manually for the first two weeks. Ten minutes of clipboard counting per store buys months of credibility.

Why the platform behind the integration matters

An integration is only as durable as the company maintaining it. Vemco Group has been doing this since 2005, founded in Denmark, and today processes more than 85 million counts per day for over 2,000 customers across 95+ countries. The platform's open, sensor-agnostic architecture means a Lightspeed retailer can start with sales-plus-footfall today and later fold in additional data sources — occupancy, weather, staffing rotas, campaign calendars — without replacing anything. For analytics teams, that openness is the difference between a point solution and a foundation. For operations managers, the practical benefit is simpler: one login, one set of KPIs everyone agrees on, and no more debates about whose spreadsheet is correct.

What to do with your first month of combined data

Resist the urge to act on day three. Let four full weeks accumulate so weekday patterns stabilise, then run three analyses: rank stores by revenue per visitor and compare against your existing revenue ranking; identify each store's two lowest-conversion peak hours; and pick one store to trial a staffing adjustment in those hours while a comparable store acts as control. That single experiment, measured in conversion points rather than gut feel, typically settles the ROI question faster than any vendor deck could.

Running your stores on Lightspeed? Connect it with Vemco and turn transactions plus traffic into conversion insight — see which hours, stores and decisions are leaving revenue on the floor. Get in touch at vemcogroup.com/contact-us and we will walk you through what the integration looks like for your store network.