Perceptual Mapping: What Do Restaurant Brands Really Mean?

Perceptual Mapping: What Do Restaurant Brands Really Mean?
by Michael Richarme, Ph.D. and John Colias, Ph.D.

Brands are conceptualized in various literature as bundles of attributes, bundles of benefits, or even bundles of promises from the producer to the consumer. These bundles can be challenging for consumers to vocalize, because often the brand meaning is intuitively known at a subconscious level. That causes a challenge for marketers attempting to describe and define their own brands, much less strategically position their brands against perceived competitors.

Brand awareness is a widely utilized metric, particularly in highly fragmented or competitive industries, such as the restaurant industry (Kardes, et al., 1993). This metric captures all the brand names that people are able to recall on an unaided basis, and scores them from those “highest in awareness” to those “lowest in awareness.” From the broad list of brands that a consumer can recall, consumers form a short list that they actually utilize in making purchase decisions, generally called the consideration set (Roberts, et al., 1997).

Consideration Set and Decisions

The consideration set may vary from situation to situation. For example, a restaurant consideration set is different for lunch with coworkers than it is for an elegant dinner with a significant other. Proximity, intervening competitors, and other similar factors can also impact a consideration set formation (Shocker, et al., 1991).

Group dynamics often come into play as well. Ask a small group of coworkers to lunch, and each person may have two or three restaurant brands that immediately jump to the top of their consideration sets. A brief negotiation process ensues, basically comparing consideration sets until a restaurant brand emerges that is acceptable to the group. An eminent social psychologist, Herbert Simon (1955), described the process of looking at alternatives until one emerged that was good enough as “satisficing.” It doesn’t involve an evaluation of all the alternatives, or even selection of the optimal choice for the group. But it is “good enough,” which typically works.

Traditional economic theory would suggest that a mathematical approach resulting in a selection of the restaurant choice with the highest overall utility score would be appropriate. Rank everybody’s choices from 1 to 5, with 1 being their first choice, and sum all the choices across the group members. It is a good theory, and a good approach for business-to-business purchases where cognitive decisions predominate.

But consumers, particularly when faced with simple or repetitive decisions, tend to rely upon heuristics. And heuristics are based on both the affective and cognitive decision processes that we all have. The emotional context of the decision (“I don’t feel like eating Mexican food today”) can be as important as the cognitive context (“We can get fast service at a reasonable price at Joe’s”). These heuristics, or rules-of-thumb, are combinations of both affect and cognition, and are readily utilized for many day-to-day decisions.

Brand Positioning Approaches

So how do restaurant executives position their brands such that when the consideration set is evoked in a consumer, or when a group discussion generates a “good enough” alternative, that their brand is selected? To borrow from an old television game show, that is the $64,000 question.

Some people use satisfaction and loyalty measures to give them an indication of how they are doing. Others use brand awareness scores for the same purpose. Both approaches give some glimpses into the minds of consumers, but often aren’t very satisfying or insightful (Lehmann and Pan, 1994).

Another approach is to create a perceptual map of consumer choices for a particular vertical market. For restaurants, it might make sense to have consumers generate an awareness map of restaurants, regardless of the industry categorizations such as quick serve restaurant (QSR), full serve restaurant (FSR), fast casual, steakhouse, etc. It might also make sense to ask consumers to describe their restaurant usage habits, such as frequency, occasion, and brand selection. And then it might make sense to ask the consumers about their evaluation of the importance of both physical characteristics and attitudinal characteristics of different attributes (lighting, meal quality, price point, length of wait, etc.). This may be a good place to start thinking about (and asking consumers about) different occasion scenarios, because an attribute lighting may be very important for some occasions and not important at all for other occasions. It is also important to gather a lot of different types of attributes, because focusing on just a few visible ones will result in everything being evaluated as important.

The next step in the process is to have the consumers evaluate specific restaurant brands across a set of attributes. Not every consumer has recent experience with every restaurant brand, so an agile and flexible computer program that only presents those brands identified in the prior usage questions is important. It is also possible to have consumers make a choice between two restaurants; this task is often a good one when attributes don’t adequately capture everything going on with a selection process or when a cross-check of the attribute evaluation process is desired. With sophisticated methodologies for setting up these programs, not every consumer needs to evaluate every possible combination, so the evaluation task can be constructed as less onerous than it otherwise might be.

Another consideration, alluded to in the opening of this paper, is that consumers may not always be able to clearly identify different attributes and the relative importance levels of those attributes. Sometimes it is as simple as a halo effect lending a certain level of importance to all attributes of a specific brand, and sometimes it is as complex as combinations of affective and cognitive factors operating below the threshold where consumers can readily or correctly identify the factors.

To help consumers with the latter situation, latent class analysis can be utilized. In this technique, the latent factors (dimensions) are identified, and brands are positioned along the different axes in relationship to each other. This technique has emerged in the past dozen years as a strong candidate to replace weaker techniques for segmentation such as cluster, factor, and regression analysis. By the inclusion of discrete unobserved variables, the analysis is able to identify perceptual distances between brands by clustering respondents along the different dimensions uncovered.

This relates closely to the concept of brand personality. Per Aaker (1997), brand personality can be defined as “the set of human characteristics associated with a brand.” Consumers personify different brands, so that one brand of restaurant might be viewed as “cool and sophisticated,” while another brand of restaurant might be viewed as “sedate and boring.”

Perceptual Maps

With this data, different perceptual maps can be constructed. As mentioned previously, the type of restaurant and the type of occasion are important means by which consumers form specific consideration sets. Therefore, a perceptual map for QSR and lunch might be an appropriate combination, as it would capture the occasion of coworkers on their lunch break.

For this paper, a perceptual map of Quick Service Restaurant (QSR) brands has been constructed, as shown in Figure 1. The data were extracted from the Decision Analyst Health and Nutrition Strategist™, a quarterly tracker of attitudes and behaviors about food, wellness, and restaurants. This database consists of 4,000 nationally representative adult U.S. respondents per year, with data collected each quarter.

Interpretation

The perceptual map shows eight major benefits that drive restaurant choice. Stronger benefits have longer rays. For example, “Good Selection of Healthy Foods” strongly drives customer choices. Rays with large angles between them indicate benefits that do not correlate and do not coincide in the mind of the customer. Based on the map, one can see that “Good Kid’s Menu/Kids Like It” and “Has One of My Favorite Foods I Can’t Get Elsewhere” do not naturally come together in the mind of the customer. The further a restaurant projects along a ray, the more customers associate that restaurant with the particular benefit represented by that ray. For example, Baja Fresh offers “Great Salads or Salad Bar” and “Good Selection of Healthy Foods.” Chuck E. Cheese’s has the strongest positioning along the “Good Kid’s Menu/Kids Like It” benefit.

Conclusions and Recommendations

From this data, it is possible to gain insight into where consumers believe (whether explicitly or implicitly) different brands are positioned relative to each other along a variety of dimensions. This may or may not match with the overt positioning strategies of the companies themselves. If there is not a match, or if a brand is hovering in the unpositioned “wasteland” near the origin of the benefit rays on the map, an opportunity exists to develop and execute a positioning strategy to draw the brand into a different, more tenable part of the perceptual map. One final consideration: all things change with time. As consumer preferences change, and as different brands alter their messaging and positioning strategies, the perceptual map also changes. This type of analysis should be done on a regular basis to identify the positioning of your brand, and to identify what your brand means to consumers.

References

Jennifer L. Aaker (1997), "Dimensions of Brand Personality," Journal of Marketing Research, 34 (August, 1997), 347-356.
Frank R. Kardes, Gurumurthy Kalyanaram, Murali Chandrashekarun, and Ronald J. Dornoff (1993), "Brand Retrieval, Consideration Set Composition, Consumer Choice and the Pioneering Advantage," Journal of Consumer Research," 0 (June), 6 -75.
Donald R. Lehmann and Yigang Pan (1994), "Context Effects, New Brand Entry, and Consideration Sets," Journal of Marketing Research, 31 (3), 364-374.
John H. Roberts and James M. Lattin (1997), "Consideration: Review of Research and Prospects for Future Insights," Journal of Marketing Research, 34 (August), 406-11.
Allan Shocker, Moshe Ben-Akiva, Bruno Boccara, and Prakash Nedungadi (1991), "Consideration Set Influences on Consumer Decision Making and Choice: Issues, Models, and Suggestions," Marketing Letters, (August), 181-98.
Herbert A. Simon, (1955), "A Behavioral Model of Rational Choice," Quarterly Journal of Economics, 69, 99-118.

About the Authors

John Colias (jcolias@decisionanalyst.com) is a Senior Vice President and Director of Advanced Analytics at Dallas-Fort Worth based Decision Analyst. He may be reached at 1-800-262-5974 or 1-817-640-6166.