Investigating Customer Satisfaction and Tip Percentage Relationships

The Foundation

For the purpose of this article, let's make the assumption that the multiple restaurants you oversee have, in some form or another, a POS system, an established holiday calendar, weather data, customer satisfaction surveys, kitchen timers, and a labor management system.

If you only have a few of these systems in place, that's okay. I just wanted to name the data source systems that could be leveraged for this reporting overview.

Asking The Bigger Question

Before diving into the relationship that customer service has on tip percentage and how that may play a role in overall profitability, let's first ask a more macro-question: What restaurant locations have negative trending sales? It's important to always ask the larger "standing far away" question before digging into the rabbit hole of possible issues and solutions.

Where to Begin

I think it would be wise to start identifying restaurants with negative trending sales during a long time frame. I'm thinking something like 6 months to a year of data should be analyzed to see if we can spot obvious trends.

We could drill down to locations that have both poor customer satisfaction reviews and tip percentages. Perhaps we can filter credit card transaction with guest counts of 4 or less. This would narrow our search and allow us to investigate our question under specified parameters.

It could help if we weed out checks that are under $10 or over $75. Focusing on the average amount spent would remove some complicated scenarios from our research.Perhaps a customer just stopped by for a drink. Or on the opposite spectrum, customers who come in and spend well above the average amount. Maybe they are celebrating something and are in a cheerful mood.

Customers tend to tip differently on Thanksgiving or Christmas. If we remove holidays, we may be able to focus on normal days with normal tipping scenarios.

Sometimes people can have bad days or celebrated days because of the weather. For example, if it hasn't snowed in years but your location receives a sizable amount, it could make some customers feel cheerful. Although this is great, it could show a spike in above average tipping because of the unexpected snowfall. If you have a packed house and it's pouring on people who are waiting outside, it could put several patrons in a bad mood, causing poor tip percentages. By filtering out days when it rained or snow, we could remove the weather from being a variable.

Digging Deeper

By this point we have narrowed down our restaurant data to show us possible relationships between customer satisfaction and tip percentage. Poor experiences usually deems poor tips. Besides finding out if we have employees who are constantly receiving poor tips, we would like to know if there are other variables at play. Below are some metrics you could dig deeper into.

• The obvious could be looking for time frames where tips are bad or good. Restaurants usually have peak times and tips are usually average during peak time because servers and staff are on their "A-game". But what about slow traffic time frames. I was shocked to find that some restaurants struggle with customer service because the tend to over or under staff just before or after their peak times. This would result in either too many employees just standing around or too little help with employees struggling to serve 6 or 7 different tables at once.
• Look at table areas of interest. If you take a look at table locations to tip percentage, you may find that there are some tables located far from the kitchen or in hidden areas where the manager and staff can't readily see. Customers who sit in these areas may experience service differently than in other areas. You may also find that the the bar always seem to do well. This could be because customers at a full-service restaurant bars are usually familiar with the staff. They may be regulars.
• If we have identified particular time frames where tips are poor, we could hone in on individual servers, hosts, and bartenders during those times.
• Could the server not even be at fault for his or hers bad tips? We could cross-reference kitchen order times with poor tips. If we notice that the kitchen staff struggles to get orders out on time during the weekend, then maybe there is an issue with the weekend kitchen staff. Keep in mind that it could be difficult but not impossible to narrow down poor kitchen employees. Most restaurants do not monitor whose hands touch a particular plate when it was being made but with a little analysis, you could find a slow cook who is struggling with new menu items (a whole new can of worms...).
• Maybe investigating the pace of service would show some type of service to tip trend. You could look at total check times from open to close and see if certain waiters have longer than average times.

Bottom Line

There are some many different variables that can affect customer satisfaction and tip percentage. The more information you collect from your data source systems, the better your analysis will be. At the end of the day, make sure you focus on the unusual trends. If you find issues, suggest a reasonable solution.

Mirus can help track and investigate. It's what we do: solve complex issues using data from multiple restaurant locations. If you don't have Mirus, you should still make an attempt to collect different data sets and monitor the information.

Thoughts?

Have you investigated this issue before? If so, what did you find?

About Mirus:

Mirus is a multi-unit restaurant reporting software used by operations, finance, IT, and marketing.

www.mirus.com

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