We bought these things last week on Amazon — I’m still not sure on whether they’re called “counters” or “tickers” or “clickers” — and we spent the whole day counting on a Saturday from open to close. Then we went home, poured us some beers, and graphed the data. We learned some awesome stuff.

Our methodology was surprisingly low-tech. Darcy had a clicker to count the number of people coming into the shop, and I had one to count the number of sandwiches coming out of the sandwich line. We recorded the data at 15-minute intervals. I annoyed Claire and she was convinced I was missing clicks. I bagged and called out all the orders while I clicked, so I also had a few interesting observations of my own.

Now I’m going to do a very awkward written presentation on some graphs I think are cool. Am I the only nerd in the room? Quite possibly.

So first up is the data Darcy collected: The number of customers entering the shop at 15-minute intervals. The yellow line is the all-time total of customers for the day, since we opened at 7:30 am to closing at 2 PM. In blue, we have the speed at which customers came in, measured in people per minute.

customer count.png

Two things we noticed here:

1) People come in little waves or bursts. It’s not steadily 100% jammed busy all day. We know this already, it’s just interesting to see it in the data. For you my dear customers: Come before 9 AM if you want a chill breakfast. It’s bananas all day after that, until 12:30.

2) We’ve served 50% of our customers by 10:30 AM. That’s wild. The business of breakfast is all about being able to capitalize on this 3-4 hour window when people feel like eating bagels the most. Talk about pressure!

sandwich velocity.png

This is the data I collected: what I am calling Sandwich Velocity, or number of sandwiches coming out every 15 minutes. You can see it’s wavy like the customer count but not a perfect reflection. Not everyone gets sandwiches, so this makes sense. The first 1.5 hours of the day are very slow on the sandwich line and the team uses it to do their prep for the day — slice cucumbers, stock cream cheese, etc. The goal is that they don’t have to stop and reload their unit halfway through service, because that REALLY slows things down. You can see there’s a big dip on the blue line around 11:30 AM — Claire ran out of bacon and had to get some while Tracy held it down alone. Our sandwich output got slower and tickets kept coming in. We’re brainstorming ways to make restocking more seamless because, let’s be real, we’re going to keep getting busier and we can only stock so much food in the front.

Here’s what we did next: We mapped the customer velocity and sandwich velocity on the same time table.

phase lag.png

Again, the waves are not perfect matches, but you can see the peaks and troughs are offset by roughly 15 minutes. I’m thinking this 15 minutes is, on average, the amount of time lapsed between a customer walking in and receiving their prepared food, inclusive of waiting in line, getting cashed out, etc. This feels like lightning speed for weekend breakfast. We probably could gather more data to study this more closely. But it’s still a super valuable insight.

I think this data really helped me see the extent to which we’re busy and the team hustles hard on the weekends. I’ve known it qualitatively because I’m here every weekend, but I had never put numbers to it until now. No wonder we’re poppin’ hard on the weekends where else can you roll in with a group of 6 for breakfast on a weekend without a reservation and have your food within 15 minutes? Even if we’re off our game and it took 30 minutes from walking in to getting the food, it beats the next best alternative, which is a 30-minute wait at a brunch joint plus a 1 hour meal at twice the price per person. We’re brainstorming ways to lean into this, so don’t be surprised if you see us testing new weekend-only items.

Do you have any ideas or insights about our weekend service? Let me know!