Restaurant Analytics 101: Focus on One Problem at a Time

analyzing restaurant data

Posted: Mar. 06, 2019

Because of its operational scale, the restaurant industry is well positioned to take advantage of big data. On a daily basis, restaurants are collecting massive quantities of information from online food delivery applications, online reservation platforms, restaurant POS systems, and digital loyalty programs. Understandably, so much data can be overwhelming.

Not too long ago, it was commonplace to check the sales reports and call it a day. As restaurant competition becomes fiercer than ever, analyzing restaurant data becomes integral to your role as manager or owner.

If analyzing restaurant data confounds you, see our breakdown of restaurant analytics below. We define the difference between reports and analytics, how to get started analyzing data, and the key data areas to analyze. Once learned, move on to the other ways you can harness the power of restaurant data to earn more and perform better.

Knowing the Difference Between Restaurant Reporting and Restaurant Analytics

As you uncover more about what restaurant analytics are and what you can do with them, it’s important to distinguish restaurant reporting from restaurant analytics (best defined in this Adobe post “Reporting vs. Analysis: What’s the Difference?”)

Restaurant reporting is the process of organizing data into informational summaries in order to monitor how different areas of a business are performing.

Restaurant analyzing is the process of exploring data and reports in order to extract meaningful insights, which can be used to better understand and improve business performance.

Restaurant reporting includes any list or presentation of core metrics (such as sales figures in an Excel doc) that are compiled to show a short- or long-term history. It’s incredibly important because you can compare sales and profits between the weeks, months, and years; find out how each dish ranks in popularity, and what the busiest day of the week is.

Restaurant analytics come into play when you want to make changes that increase your bottom line and efficiency levels. By reviewing data and finding connections, you can improve a restaurant’s performance, the customer experience, the BOH’s efficiency, and even generate targeted marketing campaigns.

Looking at key performance indicators (KPIs) is one way to analyze restaurant data. For instance, a manager that follows these four restaurant sales KPIs has a complete picture of why the restaurant is earning what it is:

  • Sales per Labor Hour
  • Food and Drink Sales per Guest
  • Revenue per Available Seat Hour
  • Table Turnover Rate 

Depending on what these KPIs reveal, a restaurant manager can make insightful changes that boost profitability, which can be measured later with restaurant reports.

How to Start Analyzing Restaurant Data

Every time a server places an order, data is created. Every time a customer pays with a card, data is created. Throughout the day, your restaurant point of sale system is accumulating data, so to avoid getting mired in it, establish your pain points.

The first step of analyzing restaurant data is figuring out which business problems you want to solve. Choose one to focus on—do this every time. By choosing one or two problems, you can correctly determine what data is needed for analysis without losing focus. The relevant data can be leveraged to solve a problem and help you maintain organization throughout the analytics process.

The second step is establishing a point of reference per pain point.

Example of Restaurant Analytics: Increasing Sales of the Worst Performing Item

For example, if you want to increase the sales of the worst performing item, you need to set a reasonable target number to monitor its performance. In this example, the target number would be the average number of times a dish is sold.

Once you have the metric (the point of reference), find out what the common traits are of the top performing dishes and see what can be transferred to the worst performing dish. For instance, if you determine that one comparison of top earning items is that they are sharable plates, and that the worst performing item is a small portion for one; turn it into a shareable plate portion. If the sales numbers inch closer to the point-of-reference metric, you have just used data analysis to improve its performance.

Example of Restaurant Analytics: Determining Which Wines to Put on the Happy Hour Menu

If you want to boost sales of your happy hour menu, track who is buying glasses of wine versus cocktails. Look at the margins of each to see just how good they are. If you find that the wine is significantly lower, the reason might be that your bar team makes superior cocktails. Or it might be that you have the wrong wines on the list. Look at how well wines that are not on the happy hour menu are selling and make changes to the happy hour menu accordingly.

There are myriad ways to analyze date to improve profitability and efficiency. Determine what the problem is and review the related data to find answers.

Types of Restaurant Data to Analyze

Although every restaurant has different paint points and goals, every foodservice operator should analyze these three data categories:

  1. Sales
  2. Customer behavior
  3. Financials

Sales data analytics keeps your finger on the pulse of the business. Analyze transactions, sales, and checks to learn what is the most popular dish, drink, or order combination. Use the data to create desirable promotions, create set menus on holidays, or create new dishes that customers will love.

Customer data analytics reveal buying behavior. Look at group sizes, reservations data, tipping patterns, and buying patterns. Too often, restaurants are using their restaurant POS like a cash register when it can also be used as a catalog of customer data. For instance, you can create customer profiles that will allow you to create successful, targeted marketing campaigns that widen your customer base, increase spending, and increase return patronage.

An example: Customer A that orders quesadillas is also likely to order guacamole on the side. With this insight, you can create a bundled menu promotion to allow for greater margins.

Financial data analytics reveal how much you are spending versus how much you are bringing in, and whether your food inventory is wisely managed. KPIs like sales per labor hour cost or revenue per employee would reveal staffing issues that are affecting your bottom line. Additionally, financial data can be used to see if food costs are at equilibrium or whether utilities are too expensive. 

Analyzing financial data used to require a pencil, paper, and calculator, but fortunately today, your restaurant POS system generates reports easily, allowing you to compare relevant data to find solutions to problem areas.

As the owner or manager of a restaurant, analyzing restaurant data allows you to correct issues, and over time, push your business in a direction where it flourishes. Ultimately, restaurant analytics determines whether or not your business is successful and why it is in the position it’s in. Use data to your advantage!

See how restaurant analytics can also help you get control over the cost of goods sold.






Posted: Mar. 06, 2019 | Written By: Emma Alois


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