Understanding Your Cover Counts

Understanding Your Cover Counts

Analyzing performance metrics in the restaurant industry often feels like deciphering a complex puzzle. Among these, the cover count—the number of individual patrons served—is one of the most confounding yet critical variables to study. "Why are there fewer customers in my restaurant than last year?" is a question that has frustrated many. Today, I'd like to share an illustrative example from a real restaurant group to demonstrate how you can tackle this enigma.

What is Cover Count?

Just to reiterate, a "cover" represents a single patron in your restaurant. Checks can have multiple covers associated to them but a single cover cannot have several checks associated to it.

Where to Locate Your Cover Count?

The primary source for this vital data is your Point of Sale (POS) system. Within your POS, the cover count is generally calculated based on seat numbers associated with each bill. So, if a single check has items assigned to four seats, that equals four covers. While some POS software does provide an option for your staff to manually input the number of patrons at a table, many restaurants disable this feature to expedite service.

What to Do When You Have Your Cover Count?

So you've downloaded an Excel sheet filled with what seems like cryptic numbers. What's next?

Contextualizing the Data

A raw cover count, when viewed in isolation, serves little purpose. To unlock its real value, you need a point of comparison. This could be:

  1. Previous Periods: How does this month's cover count compare to last month or the same month last year?
  2. Budgets: How does your current cover counts compare to what you have budgeted?
  3. Daypart: How are your covers distributed by daypart? Is there a particular hour or set of hours that holds the majority of your covers?

Analysis Methodology

  1. Trend Identification: Using your Excel data, plot the cover counts over different periods to identify trends—upward, flat, or downward.
  2. Data Segmentation: Break down the data further by days of the week, daypart (lunch, dinner), or specific holidays.
  3. Correlate With Other Metrics: Cross-reference with other KPIs like average check, table turnover rates, and customer reviews.

Real-World Case Study

Let's take an example from a restaurant group I recently consulted. Like most restaurants, this group saw a dramatic drop in covers through COVID. They hoped that their covers would come back to pre-COVID levels but they were showing a 13% decline between 2023 and 2019.

Here is how I analyzed the situation:

  1. Seasonality was ruled out as the cover decline has been steady since the beginning of 2023.
  2. Customer reviews had remained stable, ruling out a sudden decline in food or service quality. There was also no indication that a recent menu price increase influenced demand as the cover change percentage remained stable.
  3. A deeper dive into dayparts revealed that nearly all of the cover decline was coming from lunch. When I asked the General Manager for his thoughts, he said that they used to have several local offices come in for lunch a couple times a week. Post-pandemic, nearly all of the office lunches had gone away. We were able to verify that information by purchasing data from their payment processor. It showed that the out-of-town zip codes represented nearly 65% of all of their lunch covers in 2019 while only 17% in 2023.

As a result of these insights, the restaurant group revamped its marketing strategy, targeting local offices with loyalty programs and special offers. Their lunch covers have turned around dramatically and the store has posted their first positive weekly cover change since the start of the 2023.

Final Thoughts and Action Steps

Understanding your cover count change is more than a data-crunching exercise. It’s a strategic approach that integrates multiple facets of restaurant operations. Here are some immediate steps you can take:

  1. Activate Data-Driven Decision-Making: If you haven't already, set up a system to regularly track and analyze your cover counts.
  2. Consult Experts: Sometimes an external perspective can provide invaluable insights. Don't hesitate to seek expert consultation.
  3. Act and Monitor: Implement changes based on your analysis and monitor the impact closely. Be prepared to adapt and modify your approach.

So, the next time you wonder why your covers are up or down, remember: the answer lies not just in the numbers but in how you analyze and respond to them.

If you would like to know more about your cover change or if you want to carry on the conversation, let's chat!


DM me here -> Derek Smith

Email me here -> derek@canopymetrics.ca



Louis-Gabriel Lafontaine-Hebert

Co-Founder @ AI-Solution | Menu Engineering, Food & Beverage | Partnering with restaurant owners to promote better customer service and productivity by creating AI products that are easy to use.

1y

Cover count is a very important metric. However, the POS having the correct data relies on every waiter entering the right number of customers into the POS when they punch their bill. Something I didn't do consistently as a waiter myself. It didn't have any impact on the food coming out or the way the bill popped up in the kitchen so it didn't strike me as something important. How do you mitigate the effect these kinds of mistakes can have on overall data? Whether it be using the reservation system's data, or better training of wait staff.

Fariz Mambu (CCP, MA, BM)

Passionate Culinary and Hospitality professional. Curating #meaningfulexperiences by means of #unreasonablehospitality.

1y

I worked in F&B in the US for a good 20+ yrs before moving to Bali. I found in most cases, segmentation of markets are very straight forward if not predictable; urban, suburban, weekday vs weekend lunch/dinner trends. Down to the restaurant concept/cuisine, we typically will know what market segments, check avg and cover counts will be. Moving on to Bali, a relatively small island similar size and region to Puerto Rico; crossing "county/regional" lines differ greatly on spending. Southern Bali, where most of the tourism industry lies can also be segmented into several lines: higher end resort areas will have very low lunch covers as guests look outside hotels and dinner sales rise when respective hotels have specialty units. Denpasar, the capital of Bali, where my cafe is, can be considered as low tourism region and mostly rely on local and online ordering business, and most importantly mid to low spending. However, the mid-low segment is far more sustainable during crisis such as the pandemic, whereas touristy areas will suffer. I realize data is data but it is good to be able to 'not' generalize it and get accurate assessments of who, what and how to sell your product. Pricing? Data segmentation is truly important.

Excellent analysis (as usual) Derek. I love your articles

Jason Hanna

Botanic Gardens Catering Co.

1y

One of these at the front desk for staff to click off when seating works fine, correspond with main meals at shifts end to cross check. 😎

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Nihil Mevada

Co-Founder | Business Strategist - Scaling 🚀 Accounting for Restaurant / Hospitality Industry | Outsourced Accounting

1y

Very insightful Derek 🚀

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