Accurately spotting top performers
To truly understand the specific strengths and weaknesses of every team member, you need data. Not just the end of shift report the POS can spit out that lists the server’s gross sales. That’s a place to start. But it won’t tell you the true story about what’s going on with your service team.
Do you know who sold the most over the course of the week? The month? The year? Do those numbers account for the number of shifts each server worked in a comparable time frame? The number of covers per shift? Their table turn time? Do you know who’s the best at selling signature dishes or category items, like appetizers and desserts? Can you quickly see what a server’s average tip percentage is compared to their co-workers? Who’s making the most of a shoddy section?
If you’re a statistical anomaly with a strong desire to improve your service team, you might be tracking some of those data points manually in a spreadsheet, gleaned from the six-foot-long print out report from your POS. Chances are, you’re not. And who could blame you? Most operators don’t have that kind of time.
A little insight goes a long way
Imagine that you have three reliable servers, Annie, Rashena, and Nate, who all seem to have happy customers most of the time. On any given day, their sales generally reflect the day’s totals, falling anywhere between first and third in server sales out of a team of six. Glancing at their shift totals and making a mental note might be the extent of your investigation. And your gut will tell you that you’ll do fine as long as you have at least one of them on the floor at any given time. But if you want to do your best, you have some more digging to do.
With a deeper dive into more granular data, you would notice that today, Rashena was your swing server so she worked fewer hours than Nate and Annie. And though Nate ended the shift with higher sales than Rashena, his per person average was actually lower than hers. Annie’s sales were highest overall, even though Nate had more covers.
So what are Annie and Rashena doing that enables them to have higher sales with fewer customers? What is Nate doing differently that results in lower per person averages? There are important answers to these questions that you can easily see when you have the right data on hand.
To go even deeper, you may want to look at the servers’ performance at the item level. With roughly the same number of days on evening shifts this month, you can see that Annie leads the pack in wine sales. By a lot. Nate does well selling your high-dollar protein items like the ribeye and filet but rarely sells appetizers.
With that insight, you may decide to make sure that Annie works in a high-traffic section on pasta night since she excels at selling wine, and wine sells at a higher rate on pasta night overall. And if you put Rashena on Nate’s regular balcony brunch shift where there’s high traffic, but not a lot of high dollar protein sales, you can give him a Friday night shift in the bar/lounge area instead and coach him on suggesting appetizers.
With just these few simple tweaks, you could significantly increase your revenue. Especially if getting a clear picture of your service team took you less time than you spend stocking paper towels in the bathrooms.
Pro Tip: Take it a step farther by asking Annie to mentor Rashena on wine sales and you’ll really boost your time to reward ratio.
