Mastering Ticket Times to Boost Bar Efficiency & Sales for Bar Owners

I'm your host , chris Schneider , the Bar Business Coach . Every Monday , we do a short episode to give bar owners a challenge for the week . One simple project that shouldn't take more than an hour that can improve your bar and your profitability . So the challenge this week is all about ticket times .

We're gonna walk through this , but what I would like you to do , if you have the opportunity to and you have the time measure your ticket time behind the bar and in the kitchen . They're both very important and both of those greatly impact your guest experience .

So when you talk about measuring ticket times and then analyzing them and trying to improve them , it matters and I think everybody realizes this . It matters because of your guest experience .

If somebody comes in and orders a drink and it takes them 15 minutes to get a drink , or they come in and they order some food and it takes 30 or 45 minutes for them to get food they aren't likely to come back in let's be real honest about that and so our goal is to get those ticket times down as much as we can to provide a great guest experience as

much as we can to provide a great guest experience . So today we're going to walk through how you measure your ticket times and then how to do the analysis on them so that you have the data you need to drive your business forward . Now , as I mentioned , the main goal of measuring and analyzing ticket times is to improve your guest experience .

But before you get there , there are three goals that we cover first , which are one a goal of this activity is to know what your ticket time is . It is amazing to me how many bar and restaurant owners I talk to and I say so okay , your food sales have been slowing down a little bit , has your ticket times changed ? And they don't know .

And a lot of the reason people don't know is because this is a manual process . It takes some time , but it's important to understand your ticket times , not only what they are , but how they differ , based upon the employee or employees working in a station and your overall sales volume .

Another goal that we achieve by doing this is it helps you understand where you have opportunities to improve . Now , a lot of people always think , well , ticket times are great when we're slow , and they get slower as we get busier . I can tell you from my experience that is maybe 50% true . A lot of bars and restaurants .

What you actually see is when they're slowest , their ticket times are just as long as when they're busiest , and when they're actually on top of their ticket times is when they're moderately busy .

The slower you are , everything tends to slow down a little bit and then you get relatively busy Everybody's on point and going and then you get too busy and everybody gets weeded and ticket times falter .

But you need to understand what's driving that and obviously our final goal here is to have this data so we can make data-driven decisions to increase profits and make our customer experience better

. So what are the action steps for this week's challenge ? Well , first , pick where you're going to start either the bar or the kitchen . If you are doing over 25% , 30% , food , start with the kitchen . But if you don't have a kitchen or food is , you know , 10% , 12% start behind the bar , because you're you're way more bar focused .

The reason why I say to start with the kitchen if you're , say , 30 , 25 , 30% plus of your sales as food , is because food makes people stay in a bar longer . I always work on the premise that somebody is going to come into a bar . Most people are going to have two drinks .

If they eat , they're going to have three , some people have four or six , some people will have one , but in general you're going to get at least one more drink out of someone if they have food . So food is a good way to promote your alcohol sales . And if food is slow , that's the number one issue with food .

Besides the , I mean , unless you're just serving frozen stuff that you're throwing in the microwave , no one's going to pay for that anymore . But if you have decent food , the thing that's going to stop customers from ordering food the most is if it takes forever to get to that . So first , pick if you're going to start behind the bar , in the kitchen .

Second , you want to approach this in a way where you're pulling tickets to look at ticket times randomly and when I say randomly I don't mean entirely randomly right , you need to do some when you're slow , some when you're moderately busy , some when you're just crazy weeded . And then you need to look at it .

Among different employees Every employee you want multiple data points on it . And that's true behind the bar , right , if you have different bartenders , you need to know it . In the kitchen , if you have different people on your line , you need to know it .

And in the kitchen this can get particularly complex because if you say you have one cook working , well , that's easy , I got one cook , I know this cook's ticket times . If you have five cooks working well , then there might be some more math involved .

Bottom line here is you want to randomize what you're doing and I say randomizing quotes because it's not that random , but you want to make sure you're getting a broad enough sample set that your data is not particularly flawed in any way .

Now the next thing you want to do and this is the easy part , right , you got to pick the times , you got to kind of plan that part out the easiest part of this is actually gathering the initial data , which all you're going to do is when a food , item of food comes up on the pass or a drink hits that server well and is up ready for a server to come

pick it up as soon as that hits and that ticket comes across with it , you grab the ticket up , you write whatever time it is right now on top of that ticket .

Then all those tickets that you've written on you collect , because the great thing about doing it this way is that ticket will tell you what time it was ordered the server and then you're putting the time that it was completed so you have all the data right there .

And this way too , you're not stuck doing too much paperwork in the middle of , say , a rush when you're grabbing a bunch of these . The thing I will caution you here , too , is make sure you're paying attention , as you're grabbing tickets , on what's on the ticket . We think about kitchen ticket times .

Some items take longer than others , and the best example for a lot of bars here is chicken wings . If you're doing fresh chicken wings , those take 15-ish minutes to fry . So if your goal ticket time in your kitchen was , say , 12 minutes , you know everything with chicken wings on it's going to be over that .

So just make sure that as you're doing your analysis and as you're pulling data points , you're paying attention to how long things take to make Behind the bar . We don't really have chicken wings that are going to take 15 minutes , but a mojito is going to take a heck of a lot more time than a Jack and Coke .

So just keep that in mind , because different products we sell have different lengths of time required to produce the product

. Now , once you have gathered all these tickets with the time written on top , before we get into the analysis side of this , I want you to go to your POS and say you did this over the course of this next week , pull from your POS hour-by-hour sales for every day in the period that you were pulling those tickets , so for that week .

And then what I want you to do is not worry about the days per se or maybe do , because Friday and Saturday are going to be different , but the idea here is that we're getting the sales volume that existed in that hour from where you pulled the ticket .

So we know that , hey , we had $100 in sales this hour and our ticket time was two minutes behind the bar , and we had $1,000 in sales this other hour and our ticket time behind the bar was 10 minutes . That'll give us a way to correlate how busy your bar is with your ticket times .

So now we have the sales hour by hour data and we have the tickets that have the time on them . We're going to go into Excel or Google Sheets and create a spreadsheet . In that spreadsheet I want you to have seven columns . Date hour so like . When I say hour , I mean like midnight to 1am or 2pm to 3pm . So what hour of a day did it take place on ?

Potentially , what day as well would be useful here , although you have the date . So day of the week , that's up to you if you want to add a day of the week column in there . But so we have day hour . Who was working ? So this would either be the name of your bartender or the name of the folks in your kitchen At that point in time .

Our next column is going to be time ordered and the column after that time served or time ready to be served . Right , it's not where our end time is , not when it hits the guest , it's when it hits the pass or when it is ready for a server to pick up from the well , and then you can create a total time column .

You can use Excel or Google whichever one you're using , to do the math there . Apple numbers too , right , that's always an option .

All of those will give you the math there , so you can just have the Excel auto calculate how long the actual time between the two times that we measured so the time it was ordered and the time it was up and then in the final column I like to put sales for that hour and that's going to give us again the volume control here and allow us to understand how

volume reacts . All this Now , once you have all that data in your spreadsheet , there are a lot of ways to analyze it and , quite frankly , just looking at the spreadsheet with that data , you will notice things .

And when we talk about analysis here , you can do anything from doing this on a just a scratch pad , you know , take a legal pad and a pencil and just work out math in your head , or or with a calculator , or you can do more advanced things within Excel or Google sheets , like pivot tables and those pivot tables .

If you don't know what pivot tables are , you can watch a video on YouTube and it's pretty self-explanatory and easy to make a pivot table . If you have a sheet set up like I'm talking about , those pivot tables allow you to just see data at an instant .

It makes life super easy , and so really , what you're looking for here is what is your average ticket time , and you can do averages by employee . You can do averages by hour of the day , by date . You have all the data there so you can make averages out of almost anything . But what is your average ? Where are you across the board ?

When are you under that average and when are you over that average ? And once you understand when or which employees generally fall over that average , you now need to come up with a plan to lower that average and you bring that down .

And then , once you have all your ticket times a little tighter and a little more closely aligned to that average , you work on moving that average down . This is an exercise I would really recommend that you do once every , say , two or three months , because it has a huge impact on your business .

And especially it's important to do this when you have new employees that are on their own , like first solo bartending shift , because it's going to help you understand what that employee is capable of and how they're fitting in , how their service matches compared to everyone else in your establishment .

So , as always , we will have this on bar business nation with a thread and everything that described it . Also there this week , though , in that thread will be a Google sheet that has these columns laid out for you . We'll embed a couple pivot tables in there to make your analysis easier , so that it's just kind of ready to go .

You may have to do some updating and things , but I'm going to make sure you guys have that tool for this week's challenge . That's going to wrap us up for today . To get even more out of our weekly challenge , make sure you join the Bar Business Nation Facebook group . There's a link in the show notes .

I make a thread there every week so we can share our challenges and successes and improve our bars together . Until next time , have a great day and we will talk again later .