This is part of NRN’s 2012 Consumer Picks special report, produced in partnership with WD Partners. The study rates top restaurant chains based on customer preferences. Visit Consumer Picks on NRN.com for more information. For the full report, including detailed rankings on the more than 100 chains, see the Aug. 6 issue of Nation’s Restaurant News.
This issue marks the second year of Consumer Picks, an industry-wide survey reflecting how customers rate select restaurant chains. Because of the success of last year’s survey, we were encouraged to make this year’s review bigger and better.
The 2012 survey has attribute ratings on 152 chains, up from 139 chains in 2011. This year we also added an extra attribute, Craveability, to measure if a chain has menu items people crave.
In addition, this year we created a separate category for Fine Dining. While this segment only includes two chains represented in the survey, it will be easier to read and review the Casual-Dining segment without having to mentally “subtract” the Fine-Dining brands. Of course, we hope to grow the number of brands included in that segment in the 2013 Consumer Picks.
This year we are also including importance ratings for Limited Service, Family Dining and Casual. It is interesting to note that the importance attributes increase as check averages increase between each of these segments. Consumers are clear that if they are paying more, they will expect more across the attribute spectrum.
These changes aside, much of the survey is consistent in format to the 2011 report in order to facilitate year-to-year comparisons. These assessments will undoubtedly be a common use of the data. As such, I would like to offer a few suggestions and warnings.
First, a warning: If you look at just the difference in a brand’s scores from 2011 to 2012, you run the risk of drawing incorrect conclusions. To look only at one year over the other for any one brand does not take into account the time difference between the research or the economic and social factors that took place during the year, impacting consumers’ responses.
A better way to make the comparison is to take one brand’s score against the average score for an identical group of competitors, comparing the relative difference between the subject brand and its group of rivals. Directly comparing how the subject brand is doing against a competitive set allows for more insightful evaluations.
In the example provided below, we use Red Robin Gourmet Burgers as the subject brand. To make the comparison, five competitors were chosen. I would recommend using a minimum of five competitors as the basis for comparison, although you can use more.
Looking at Red Robin’s Overall Score in isolation shows almost no change — 66.5 percent in 2011 versus 66.6 percent in 2012. But when you look at these scores against the competitive set, where the average score dropped from 63.9 percent to 61.4 percent, a much more noticeable improvement in Red Robin’s relative position becomes apparent.
This same pattern can be seen in the Food Quality attribute. In the Service attribute it may appear that Red Robin had a lower performance in 2012, yet in comparison to the competitive set, the casual-dining brand actually improved by a slight degree.
If you would like to make a similar comparison of your brand, WD Partners has created a blank Excel file that you can download. It is available at: www.wdpartners.com/lombardi/downloads.php.
Finally, when reviewing this survey data, please keep any one brand’s set of attribute ratings in perspective with the brand’s positioning and historic offer. For example, In-N-Out Burger scores somewhat lower for Menu Variety, but a limited menu is part of that brand’s positioning strategy and needs to be taken into account.
Likewise, when looking at the Atmosphere attribute ratings for Papa Murphy’s Take ‘N’ Bake Pizza, it is good to keep in mind this is a takeout-only brand where there is very little time spent inside the store, making the Atmosphere attribute less critical.
As with any other data, the more the user understands the correct interpretation of the results, the more value the data provides.