The most expensive argument in retail
Sales are down, and the meeting splits into two camps: marketing says the store is not converting the traffic it is given, operations says there is no traffic to convert. Both sides are arguing from the same monthly sales figure, which cannot settle it, and the cost of guessing wrong is a quarter spent on the wrong intervention. Two numbers settle it in minutes, provided the store measures visits at all.
Is it a traffic problem or a conversion problem?
Compare visits and conversion rate against the same period last year or the prior stable baseline. Visits down and conversion flat is a traffic problem: fewer people arrived, and the store performed normally on those who did. Visits flat and conversion down is a conversion problem: the same demand arrived and less of it bought. Both down means a traffic problem that is also depressing conversion, usually through understaffing or reduced hours. Both up with sales down points at basket size rather than either, which sends you to basket size vs footfall and sales per visitor.
The decision tree
Branch 1, visits down, conversion holding. The store is fine and the catchment or the marketing is not. Check capture rate to separate "fewer people passed" from "fewer of the passers came in", which are different problems with different owners: retail capture rate and, for the storefront half, window conversion rate. Falling passing traffic is a location and catchment question; falling capture with steady passing traffic is a storefront and window question.
Branch 2, visits holding, conversion down. Something inside changed. Work the funnel in order rather than guessing: did zone reach change (layout or a relocated category), did trial engagement drop (fitting rooms, demos), did queues lengthen, did staffing coverage at peaks fall? The stage-by-stage method is customer journey mapping with the KPI set in customer journey KPIs, and the levers in how to increase retail conversion.
Branch 3, both down. Treat traffic first but check staffing immediately, because the standard response to falling traffic is cutting hours, which lowers conversion further and accelerates the decline. This is the loop worth naming out loud in the meeting.
Branch 4, neither down but sales down. Conversion and visits are healthy, so the problem is what people buy, not whether they buy: pricing, mix, or attachment.
Before you trust the diagnosis, check the instrument
Every branch above depends on the visit count being real. Three failure modes produce fake diagnoses: unexcluded staff movements inflating visits and depressing conversion (staff exclusion), a counting drift nobody noticed making a trend out of a calibration problem (calibration and the silent-failure detection in maintenance and SLA), and a comparison window that ignores day-of-week or seasonal shape (day-of-week footfall). Rule those out before anyone reorganizes a floor.
Run it as a standing report, not a fire drill
The diagnosis is more valuable monthly than in a crisis, because both branches are cheaper to fix early. A standing view of visits, conversion, and capture rate per store, read together rather than separately, catches the divergence in the month it starts. The formula and the reading discipline are in the retail conversion rate formula, and what a defensible baseline looks like is in retail conversion benchmarks.
Where Ariadne fits
The diagnostic needs three measured numbers (passing traffic, visits, transactions) and most stores have only the third. Ariadne supplies the first two camera-free, which is what turns this argument into a two-minute check.
