What does pairing footfall with sales give you?
Visitors per store per hour from the people-counting integration, joined to the transactions from the same store and hour. That produces conversion per trading hour: the share of visitors who bought. It is the retail equivalent of an online conversion rate, and it is the figure a store admin cannot produce because it has no idea who walked in.
With conversion per hour, a Saturday afternoon that felt busy can be shown to have converted worse than a quiet Tuesday, which is usually a staffing or a stock problem rather than a demand problem.
- Visitors per store per hour, from the people-counting integration.
- Conversion per trading hour: the share of those visitors who bought.
- Basket size and units per transaction, so a strong conversion can be read against what it sold.
- Peak hours per store per weekday, which is what the staffing plan is built from.
- Lost sales in busy hours, priced and fed into the cash-leaked figure on the money-at-stake strip.
How are lost sales in busy hours computed?
When a store's conversion drops in its busiest hours, the difference between the hour's conversion and the store's own best conversion, applied to the footfall in that hour and the store's average basket, is the sales the hour did not serve. The engine computes it and prices it, and it feeds the cash-leaked figure on the money-at-stake strip.
The assumption is stated on the surface: it compares the store to itself at its best, not to another store or an industry figure. That keeps the number defensible in a conversation with a store manager.
Why does the staffing plan cost nothing extra?
The plan takes the store's existing rostered hours and proposes moving them toward the hours where conversion falls under load. It does not propose adding hours, which is why it is cost-neutral: the same wage bill, distributed against the footfall the store actually receives.
The plan is a proposal to the store manager, sent through Review & Send with the arithmetic attached, and the following weeks' conversion is what tells you whether it worked.
Which store wins, and why
Ranking stores by revenue rewards the biggest catchment. Ranking by conversion per trading hour rewards the team and the stock. The engine shows both, plus basket size and units per transaction, so an owner can see whether a strong store is strong because of its location or its execution.
That comparison is also how a good practice spreads: a store converting far above the chain in the same hours is doing something the others can copy.
How does website friction sit next to shop conversion?
Where a web behaviour analytics tool is connected, the engine reads page, source and device behaviour and deep-links to the recordings in that tool rather than rebuilding them. Online friction is read next to shop conversion, so an owner sees a brand's whole funnel rather than two separate ones.
The engine does not try to replace the behaviour tool. It joins its signals to commerce so a drop in mobile checkout completion can be priced in the same dollars as a bad Saturday in a shop.
Which role owns store traffic on ftes.ai?
The Store Officer FTE owns physical retail performance and is judged on sales per trading hour. It proposes the staffing moves, the stock transfers that busy hours justify, and the clienteling follow-ups, all through Approvals.
A brand with shops and no people counting sees the surface say the integration is missing rather than reporting zero visitors. Connecting one store is enough to see whether the join is worth rolling out.
What does a store manager actually receive?
A short message with three things: the store's conversion per trading hour for the week against its own best, the hours where conversion fell under load, and the proposed move of existing rostered hours. The arithmetic is attached so the plan can be argued with.
Nothing is imposed. The message goes through Review & Send from the owner or the retail lead, and the following weeks' conversion is the measurement that says whether the move worked, recorded in Results like any other action.