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Top Shops Benchmarking Survey — FAQs

Products Finishing’s Top Shops Benchmarking Survey is gearing up for 2023. Jan Schafer, director of marketing research for Gardner Intelligence, offers insights for taking the survey and interpreting the results.

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American Tinning and Galvanizing was recognized as a Top Shop in 2023.
Source: American Tinning and Galvanizing

Top Shops is a tool in your toolbox. It is one source of data, albeit a robust collection of metrics that reveals tons of information that is otherwise not available. While it is impossible to make the survey a perfect fit for every size and type of finishing shop out there, it is designed to help you understand where you stand in the industry. Approaching the Top Shops report with this in mind, you can generally make sense of numbers that may not make sense at first blush and/or compare back to your own past surveys. It is also important to keep in mind how your shop is different. It is fair to factor in those differences when you are interpreting Top Shops results.

Q. My business has several locations. Do I answer one Top Shops survey for all of them combined, or complete separate surveys for each location?  
A. Answer according to how you keep your books. If metrics like profit margin and sales revenue are combined across locations for managing the business, that is how you’ll want to submit your answers. If, on the other hand, the books are kept, and only kept, by location, you should complete separate surveys for each.

Q. My shop is small. We have just one manager/owner and six other employees. How can we possibly compete for Top Shops status when we are compared to shops multiples of our size?
A. We do what we can to put shops on a level playing field when it comes to size. Many questions with numeric responses are worded in ways that take facility size out of the equation. That means asking survey participants to calculate answers that divide values (e.g. sales revenue) by number of employees and/or number of machines. Sometimes we do those calculations behind the scenes, using values participants provide. Either way, this practice achieves a degree of ‘equalization’ across shops. 

Q. I cannot answer all of the questions. Some just do not apply to me/my business. Does that count against me?  
A.
Honestly answer all Top Shops questions that you can. Skip those that you really cannot answer or select ‘does not apply’ if that response option exists for the question (it sure should, whenever ‘does not apply’ is a real possibility.)   

Q. Can you just tell us which questions are scored?  That way we can be sure to answer the questions that count.
A.
Score-based top shops status is just one objective of the program. Benchmarking is another key objective. Answering all questions contributes the most to Top Shops’ objectives. If we were to ask just scored questions, some of the diagnostic ‘why’s’ would be lost. There is also the risk that it becomes easy to game the system.  

Q. Shops are not created equal in terms of the work they do. I am familiar with a few Top Shops near me, and some of them are super specialized doing fantastic work manufacturing only a few ‘intricate’ parts. Our business is large quantities of simple parts. I would expect a specialized shop to score better every time.
A.
The way we ask questions and interpret results acknowledges those types of differences. We do our best to put everyone in the same mindset with formulas and examples to not advantage or disadvantage shops based on profile information, including industries served. 

Q. How can you be sure everyone answers honestly? Oftentimes, when I go through my custom report, I seriously question some of the values submitted. They seem too good to be true.
A.
Answers are self-reported according to the honor system. Benchmarking relies on honest answers, and our observation is that most survey participants support that objective. We do remove the occasional outlier with extreme or otherwise fraudulent responses.

Q.  How can it be that a super high value lands in just the 80th percentile?  
A.
It is typically a function of the metric having a lot of participants clustering around the same (often very low or very high) value. Examples are accident incidents and turnover rates. Everyone works on having low values for these metrics, so there is little distribution of responses. 

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