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Turn the data you already capture into sharper forecasts.

Most retailers sit on more data than they use. We combine it with the outside signals that move footfall, then turn the result into decisions managers can act on each week.

What it is

What is retail workforce analytics?

Retail workforce analytics connects the data your point-of-sale, scheduling and footfall systems already produce, enriches it with external signals, and turns the result into operational decisions: where to add or remove hours, which stores are mis-rostered, and where service is at risk.

A dashboard nobody acts on is a cost, not an asset. The value is in the decision it changes. We work backwards from the decisions that move labour cost and service, then build only the data foundation those decisions need.

Crucially, your own history only explains part of why customers show up when they do. Pairing it with external signals is what lifts a forecast from roughly right to reliably right.

From signals to a sharper forecast

Internal data and external signals, working together.

We combine the data you already hold with the outside factors that move demand. Together they explain footfall your own records never could on their own, and that flows straight into better rosters.

Internal data you already hold

SalesTransactionsFootfallLoyalty

External signals we add

WeatherDay of weekSeasonDiscountsLocal events
Demand model
Learns each store
Store-level forecast
Hour by hour
Roster decision
Cover that fits

Combining the data your systems already capture with external signals such as weather, day of week, season, discounts and local events sharpens the demand forecast every roster is built on.

The efficiency scan

What the no-obligation scan gives you.

The free first step shows you what is already in your data and what it could be worth.

A data inventory

What you already capture across POS, scheduling and footfall, and where it currently sits in silos.

The decisions it could change

The handful of weekly rostering decisions that better data would actually move.

A joined sample view

Labour, demand and service joined for a sample of stores, by store and daypart.

A pragmatic roadmap

What to connect first for the fastest, most measurable return, with no new platform assumed.

Outcomes

What good looks like.

1 view

Labour, demand and service joined by store and daypart instead of separate silos (illustrative).

Weekly

Cadence at which the outputs change the roster, not just describe it (illustrative).

Owned

Built on the data you already own, with no new platform required (illustrative).

Questions, answered

Data & analytics: common questions

Usually not. We start from the systems you already have and add only what the decisions require. A new platform is a recommendation, not a default.
Footfall is driven partly by things inside your data and partly by things outside it. Adding weather, day of week, season, discounts and local events lets the forecast explain swings your own history alone cannot.
We work backwards from the rostering decision, not the dashboard. The test is whether next week’s schedule changes, not whether a chart looks good.

Find your labour-cost gap in one conversation.

A no-obligation efficiency scan gives you a numbers-first picture of where your roster is leaking margin, and what it's worth to fix. No software to buy. No commitment.

No obligation No software to buy A clear business case
Prefer to talk first?
K. Kropf
Founding Partner, MSc Computer Science