Restaurant Analytics: Making fast casual brands grow even faster

March 9, 2016

By Peter Chen, Director of Data Science at Algebraix Data

Fast casual eateries are the Usain Bolt of the restaurant business. Like the triple-gold Olympic champ, they're not just quick, they're also super-successful and wildly popular. These regional and national restaurant chains — think Panera, Shake Shack, Qdobe, Noodles & Company, Rubio's and Wingstop — have figured out how to combine speed with made-to-order but affordable food. They're huge growth stars in the restaurant world.

But when it comes to data, many are stalled.

Why? Fast casual restaurant owners aren't any different from owners of other businesses: They believe data has its place, but they're convinced that nobody — and certainly no software program — could ever know their business better than they do. They trust their gut instinct honed by years of experience. Because when it comes to running a profitable restaurant, they'll swear nothing tops time in the trenches.

Improving successes, preventing mistakes
But data science is changing that view. Terms like "predictive analytics" and "data-driven insights" are becoming part of the smartest restaurant-business conversations. True, for decades, restaurateurs had to rely on instincts and experience. Today, it's both possible and essential to get outside, unbiased, data-driven information about the present and future of your business. In fact, owners can no longer afford to just trust their guts. Here's why: As you build experience, you inevitably also build up biases, whether they're about the "best" people for servers or which dishes "always" sell. However, even once-valid biases eventually go out of date, and intuition simply isn't reliable. Pause and think about how many mistakes you've made when relying on your gut instincts, as much as you'd prefer to forget them.

Ego-free idea testing
Now, imagine coming up with what you suspect is a brilliant idea AND having a way to test it against data. Analytics can't come up with ideas, but it can help you improve on good ones, avoid trying bad ones, and uncover flaws that can be fixed. In other words, it can help make good ideas great and keep mistakes from seeing the light of day.

When you use analytics to get a data-driven assessment of your business, it erases biases, preconceived notions, and delicate personalities; algorithms have no ego. Plus, analytics need to be reinforced by an outside data scientist, who has no stake in your menu items or staff size. His only goal is to help your business succeed. When you do well, he does well.

Gaining a competitive analytics edge
Maybe you're still not ready, but let's say your closest competitor has hired a consulting firm to run restaurant analytics and have a data scientist interpret the results. That company is already analyzing your rival's data on everything from payrolls to parsley. What is the competition learning that you're not?

A lot. For example, let's narrow-focus on an area critical to every restaurant, especially fast casual chains: pinpointing your most/least profitable times of the day and days of the week. I bet you're sure you know the answers. But, are you right? If so, do you know why? And most important, do you have reliable, tested insights on how to improve the results?

For instance, do you know whether it would be smarter to try improving your weaker business times/days or to focus on making the strong times/days even more robust? Similarly, do you know which menu items are most popular with repeat customers?

Those are just some of the things that truly competitive restaurants are learning from analytics and using to drive performance. And because no industry is the same, they're using analytics that are specific to the restaurant business, which focus on challenges like:

  • Menu optimization.
  • Customer segmentation.
  • Staff optimization.
  • Operations improvement
  • Time of day and day of week analysis

2 things you never want to hear yourself say
1. "Our morning business is booming. Who knows why?" This is an example of an easily made mistake that a good data scientist would quickly spot while looking for insights into a restaurant's data. Say there's been an aggressive push to encourage customers to phone in take-out orders ahead of time. That can easily throw off "peak hours" data. It looks like the AM business has finally taken off, when, actually, it's just that lunch orders called in early are being logged as breakfast business.

2. "This restaurant is different from all our others. Who knows why?" Let's suppose that dinner is the biggest business in all of your fast-casual restaurants. Then, you expand into your first shopping mall. It's a big, bustling place, but your dinner business is a nightmare. It's not the manager; it's the location. As predictive restaurant analytics could have told you, dinner receipts typically plunge in a food court.

What's holding you back?
One of the main reasons companies do NOT act on analytics is the fear of how much work it will take just to figure out and set up the initial systems — and double that when your expertise is running a restaurant, not analyzing data. I'm totally with you. As a data scientist, I'd shudder at just the idea of trying to set up a restaurant. So, you won't be surprised by my advice: Don't even think about trying to be a DIY data scientist or analytics expert.

That's exactly why "analytics as a service" makes sense for restaurateurs (and why ordering dinner from you makes sense for me). You want an affordable firm that will do the heavy lifting for you and that includes a data science team.

That way, you can focus on USING the insights and forecasts restaurant analytics give you, not on setting up and staffing a data-analysis operation. That would be like having me design a restaurant's menu and setting up its kitchen. Not a good idea!

 

 


Topics: Systems / Technology


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