Conversion is the number of receipts divided by the number of visitors. Everyone knows the formula. Almost no one knows where the denominator comes from, and that’s where most mistakes hide: staff counted as customers, groups that buy on a single receipt, a monthly average that hides the hour when you lose half your visitors. Below: how to calculate it correctly, what the number means and why comparing yourself with “the industry average” doesn’t help.
If 400 people came in yesterday and 100 receipts were issued, the day’s conversion is 25%. The hard part isn’t the division; it’s what you put into each part of it.
You count receipts, not items sold and not their value. One receipt means one purchase decision. If a family of four shops together and pays once, that’s one receipt — but four people came in. That’s why a store where families shop together always shows lower conversion than a convenience store, without its sales staff being any weaker.
This is where it all breaks down. If the count includes staff, couriers, sales reps and the customer who stepped out to the car and came back, the denominator grows and conversion drops — without anything real having changed. In a small location, staff can make up ten percent of everyone who crosses the threshold.
Enter yesterday’s figures for one location. Receipts cannot outnumber visitors: if yours do, the count is missing people.
Calculation: receipts ÷ visitors × 100. One extra point means visitors × 1% × 30 days × average basket.
Measure your real conversion →We see all four in chains that already have counters installed and nice-looking reports.
A salesperson who steps out ten times a day adds ten visitors who will never buy. The effect is small in a hypermarket and huge in a pharmacy.
Published figures come from systems that count differently, in stores of a different format, in another country. Compare yourself with your own numbers from last week, and with the store one street over, measured with the same method.
The monthly average is reassuring, and you can’t act on it. The loss sits in two or three hours of the day, and averaging makes them disappear. Read conversion by the hour, not by the month.
Rain, holidays, payday and street construction shift traffic by double-digit percentages. A lower conversion rate on a high-traffic day can actually mean more sales.
Once conversion is broken down by hour, the picture changes completely: the same 25% day can be 34% in the morning and 12% between five and seven in the evening. The 12% hour isn’t a sales problem; it’s a queue, staffing or stock problem — and it can be fixed that very week.
Not “the month was weaker” but “on Tuesdays and Thursdays, between 17:00 and 19:00, 40% more people came in and left with the same number of receipts as usual.”
The same camera shows how many people were at the checkout and how long they waited, how many employees were on the floor, and whether anyone set foot in the promo zone.
A third checkout opened in that window, one person moved from the stockroom to the sales floor, the promotion taken out of the corner nobody walks past. The following week you check it against the same number.
Conversion tells you how much you lost. The heat map tells you where. It’s an image of the floor plan in which the spots where people stopped are colored in, and the places nobody walked past are left cold.
The cold zones at the far end of the floor, the corner behind the pillar, the shelf by the exit. If the goods there don’t sell, it doesn’t necessarily mean nobody wants them: maybe nobody walked past them. These are two different problems, with two different solutions.
The map is built from counted silhouettes, not identities. Nobody is recognized, no profile is built, and no path is linked to a named person. Images are processed on the Vizor server. The details are on our privacy page.
Conversion by hour, bypassed zones and checkout queues, in every store in the chain.
Read →Infrared beam, 3D sensor or your existing cameras: what each one measures and what it costs.
Read →How many customers a consultant greets, how long they wait and which collections they skip.
Read →How many drivers walk into the shop, how many buy, and on which shift sales are lost.
Read →Footfall is the number of people who come in over a period: per hour, per day or per week. It is counted at the entrance, with a counter or from the camera at the door, with staff subtracted so that only potential buyers remain. Set against the number of receipts for the same hour, footfall gives you the conversion rate.
There is no single figure that holds for every store. In a pharmacy most people who come in buy something: in our pilot, about two in three. In clothing, furniture or electronics stores many people come in just to look, and conversion is much lower. Compare the store with itself, by hour, by day and across locations, not with an average from another country.
Divide the number of receipts by the number of visitors in the same period and multiply by one hundred. If 400 people came in and 100 receipts were issued, conversion is 25%. You count receipts, not items sold and not their value: one receipt means one purchase decision, even if a family of four paid.
It’s the ratio of people who bought to people who came in. It tells you how well a location turns traffic into sales, regardless of how much traffic it gets. A store with low traffic and high conversion has a visibility problem; one with high traffic and low conversion has a problem inside — the queue, staff, stock or product placement.
There’s no universal number, and we advise you not to compare yourself with published averages. They come from systems that count differently, in stores of a different format and from another country — and if the denominator is calculated differently, the numbers aren’t comparable. The useful comparison is twofold: you against last week, and your locations against each other, measured with the same method.
Most often because the visitor count includes people who had no way to buy: employees going in and out, couriers, sales reps, the customer who went back for their wallet. In a small location, staff can be a tenth of everyone who crosses the threshold. The second common cause is groups: four people paying with a single receipt look like 25% conversion.
With a counter at the door, or with video analytics on the security cameras you already have. The difference between them, with the advantages of each, is explained on our page about the people counter. What matters is that the method is the same in every location; otherwise, comparing stores means nothing.
Because the average hides exactly what you care about. The same 25% day can be 34% in the morning and 12% between five and seven in the evening. By the month you see nothing and can do nothing; by the hour you see the window in which you lose people, and you can fix it the same week — an extra checkout, one person moved to the sales floor, a promotion taken out of the corner.
An image of the floor plan in which the spots where people stopped are colored in and the places nobody walked past stay cold. It shows whether goods that don’t sell really aren’t in demand, or simply aren’t seen. It’s built from counted silhouettes, with no identities: nobody is recognized and no profile is built.
Software that reads the security camera feed and extracts numbers from it: how many people came in, by hour; how many bought, against receipts; how long they waited at the checkout; where they walked on the sales floor. It runs on existing cameras, needs no new devices at the door, and sends numbers, not images, out of the location.
We look at the cameras you have and your receipts by hour, and show you the location’s true conversion rate — with staff taken out of the denominator. We reply within 24 hours, in writing.