Alright, let’s talk about figuring out foot traffic. We run a small retail spot, and for the longest time, we were just guessing how busy we actually were at different times. Felt like we needed real numbers, not just ‘oh, it feels busy today’.
Getting Started – The Why and How
So, the first thing I did was sit down and think, okay, what do we really need to know? Basically, how many people walk through that door, and when are they doing it? Seemed simple enough.
My first attempt? Super basic. I literally tried using one of those hand-held clicker counters for a day. Stood near the entrance and clicked every time someone came in. Man, that got old fast. And I missed people when it got busy, or if I had to help a customer. Plus, I couldn’t do it all day, every day. Not practical.
Trying Out Some Tech
Next step, I looked into some simple tech solutions. Found those infrared beam counters. You stick one piece on one side of the door frame, the receiver on the other. Someone walks through, breaks the beam, counter goes up by one. Sounded easy.
- I bought a cheap set online.
- Installation was straightforward, just stuck them up with adhesive tape.
- Powered them on, and yeah, they started counting.
But here’s the problem: they weren’t very smart. If two people walked in together, maybe holding hands or just close? Click – only counted as one. Someone pauses in the doorway? Might count them twice, or not at all. And forget about accuracy if people are going both in and out through the same door close together. The numbers were all over the place, not reliable enough.
Moving to Cameras
Okay, beam counters were out. I needed something better at telling individual people apart. Started researching people-counting cameras. Not the regular security cameras, but ones designed specifically to count heads from an overhead view.
Found a system that wasn’t crazy expensive. It involved mounting a camera directly above the entrance, looking straight down. This seemed way more promising because the top-down view makes it easier to distinguish individuals, even in a small crowd.
The setup process went something like this:
- Picked the right spot: Directly centered over the main entrance area.
- Mounted the camera: Had to drill a few holes, run a power cable and a network cable up into the ceiling space. A bit fiddly, but manageable.
- Connected it: Hooked it up to our little network switch.
- Configuration: Logged into the camera’s setup page on my computer. Had to define the ‘counting zone’ – basically drawing a line across the doorway on the video feed. Told it which way was ‘in’ and which way was ‘out’.
Getting the Data and Using It
This camera system was much better. It spat out ‘in’ and ‘out’ counts. Most of these systems have some way to access the data. This one let me schedule an automatic data export – it would save a simple data file (like a CSV) with timestamps and counts to a shared folder on our network every hour.
Then, I put together a really basic spreadsheet. Imported the data files each day. Took a bit of manual work initially, but then I automated it a bit more using some simple spreadsheet functions to pull the data together.
And boom! We finally had real numbers. We could see clear patterns: which days were busiest, pinpointing the exact peak hours. Turns out, our assumptions about busy times were sometimes way off.
We started using this data to plan our staff schedules much more effectively. No more overstaffing during dead quiet periods, and making sure we had extra hands when the data showed us the rushes actually happened. We could also kinda see if a new window display or a sidewalk sign actually pulled more people in compared to the week before. It wasn’t rocket science, just practical data we could finally use.
It took some trial and error, starting with the clicker, failing with the beams, and finally landing on the overhead camera. Wasn’t perfect from day one, but we got there. Now we have a much better handle on our store’s pulse.