Vemco and SameSystem: Staff Scheduling Powered by Real Footfall Data

Most retail schedules are built on a proxy: last year's sales curve. The problem is that sales tell you when people bought, not when they arrived — and if your Saturday morning was understaffed last year, the sales data from that morning quietly encodes the understaffing into next year's plan. You end up scheduling to your own past mistakes. Footfall breaks that loop, because visitor counts show demand regardless of whether you converted it.

That is the practical reason the Vemco and SameSystem integration exists. Staffing is the biggest controllable cost in retail, and footfall is the best available predictor of when staff are actually needed. If you are already planning your workforce in SameSystem, the question is not whether traffic data would improve your schedules — it obviously would — but how to get it into the planning workflow without another manual export routine that dies after three weeks.

How the two-way integration actually works

This is a genuine two-way connection, and both directions matter for different teams.

• Direction one: Vemco into SameSystem. Footfall data flows from the Vemco platform into SameSystem, so store schedules and staffing levels are planned against actual visitor traffic patterns. Your planners see the measured traffic curve per store, per day, per hour — and match staff hours to the hours when visitors actually show up.
• Direction two: SameSystem into Vemco. Sales data flows from SameSystem back into the Vemco platform, where it is combined with footfall to produce conversion rates, revenue per visitor and store performance analytics. This is where operations and commercial teams get answers, not just numbers.

The second direction is the one evaluators tend to underrate. A traffic feed into your scheduling tool is useful. But sending sales the other way turns Vemco into the place where the staffing question gets settled with evidence: when a store misses target, was it a traffic problem (fewer visitors than expected) or a staffing problem (plenty of visitors, but conversion collapsed during specific hours)? Those two diagnoses lead to completely different actions — one is a marketing and location conversation, the other is a rota conversation. Without combined data, that argument happens on opinion in a Monday meeting. With it, the answer is on a dashboard before the meeting starts.

What changes in the weekly scheduling routine

For a store operations manager, the shift is concrete. Instead of copying last year's rota forward and adjusting by feel, the planner in SameSystem sees measured traffic curves and schedules against them. Two patterns become visible almost immediately in most chains:

• Overstaffed quiet hours. Typically early weekday mornings and the last hour before close, where habit — not demand — has kept two or three people on the floor. These hours are pure recoverable cost.
• Understaffed peaks. Short, sharp traffic spikes — often lunch windows and late Saturday afternoon — where conversion drops because queues form and advice-dependent categories go unserved. These hours are where lost revenue hides.

The financial logic is asymmetric and worth spelling out to your CFO: hours trimmed from quiet periods reduce cost directly, while hours moved into peaks recover revenue you were already paying rent and marketing to attract. Reallocation, not headcount reduction, is usually the first win — the same wage budget, redistributed to match the traffic curve, before anyone touches total hours.

A note from implementation: watch the interval, not just the total

Here is something teams consistently learn a few weeks in: daily footfall totals are almost useless for scheduling. Two stores can have identical daily counts and need completely different rotas, because one has a flat curve all day and the other does 40% of its traffic in three hours. The value sits in the hourly — sometimes half-hourly — distribution, and in comparing that distribution across weekdays. Experienced implementers also flag a second trap: the first two weeks of traffic data will contradict what store managers "know" about their store, and that friction is normal. Let managers see the raw curves themselves rather than announcing conclusions from head office. Managers who discover their own quiet Tuesday morning in the data accept the rota change; managers who are told about it by a regional dashboard push back for months.

Why data quality decides whether any of this works

Scheduling against traffic only makes sense if the traffic numbers are trustworthy enough to override human intuition — because that is exactly what you are asking store managers to do. Vemco Group, founded in Denmark in 2005, processes more than 85 million counts per day across 2000+ customers in 95+ countries, and commits contractually to a minimum of 96% counting accuracy — in practice typically 98–99% when conditions such as lighting, store layout and visitor behaviour allow. That contractual floor matters more than the headline figure: it means accuracy is an obligation you can hold your provider to, not a brochure claim measured on one flagship store.

Equally relevant for chains with mixed estates: Vemco is a sensor-agnostic, open data analytics platform. If your portfolio already contains counters from several hardware brands — a common legacy of acquisitions and phased rollouts — you do not need to rip and replace before connecting SameSystem. Sales data from SameSystem can be combined in Vemco with footfall from any sensor brand, plus IoT and other data sources, into one consistent analytics layer. The scheduling logic stays the same across the whole chain even when the hardware underneath does not.

The operational payoff: no manual data moves

Anyone who has run traffic-based scheduling on spreadsheets knows how it degrades. Someone exports counts weekly, pastes them next to the rota, and it works — until that person is on holiday, the export format changes, or a busy trading period pushes it down the priority list. The integration removes the manual step in both directions: footfall arrives in SameSystem for planning, sales arrive in Vemco for analysis, continuously and without anyone touching a CSV. For HR and workforce planning teams, that reliability is the difference between a pilot that impressed once and a scheduling method that survives staff turnover, peak season and year three.

There is also a fairness dividend that workforce teams appreciate: schedules justified by measured demand are easier to defend to staff than schedules justified by "that's how we've always done it". When an employee asks why Thursday evening got an extra shift and Tuesday morning lost one, the traffic curve is a better answer than a manager's instinct — and it depersonalises rota changes that would otherwise feel arbitrary.

Where to start

If SameSystem is already your workforce platform, the sensible pilot is small and measurable: connect a handful of stores with contrasting traffic profiles, run four to six weeks of footfall-informed scheduling, and compare wage cost per visitor and conversion by hour against a control group still on the old method. The gaps between scheduled hours and measured traffic will tell you within the first month whether the rest of the chain should follow — and in most chains, they do.

Planning your workforce in SameSystem? Connect it with Vemco and schedule against real traffic instead of last year's assumptions. Talk to our team about the SameSystem footfall integration at vemcogroup.com/contact-us.