Techniques for Perceptual Mapping V0.1.1

FEBRUARY 2017 TECHNIQUES FOR PERCEPTUAL MAPPING I. INTRODUCTION If consumers are aware of our brand but As a firm tries to manage its brand’s don’t consider it, or if they consider it but equity, it leverages how the brand is don’t purchase it, we could investigate how perceived relative to the competition. the brand is perceived through corporate Correcting perceptions of weakness or image research. taking advantage of perceived strengths can One form of corporate image research shows both strengthen the brand’s position comparisons among brands on specific in the marketplace. product features or attributes. Graphs that Frequently, brand research is centered on a show the market’s perceptions of brands or hierarchy of eects model as shown below. companies are called perceptual maps. Although slightly dierent models have been Many dierent techniques have been proposed in dierent industries, they follow developed to produce maps reflecting the same general pattern. consumers’ perceptions. The more sophisticated maps show relationships A potential buyer becomes aware of a among brands, among attributes, and among product, and then becomes interested in brands and attributes. the product. Then the buyer considers purchasing the product. The potential buyer Here, it appears that Brand A is better than becomes an actual buyer with the purchase competitors at being considered, but rarely of the product. He or she then evaluates purchased. Brand D, however, is less likely to the purchase and whether or not to buy be considered but among those considerers the product again. is much more likely to be purchased. These graphs would suggest that Brand D has a In many industries, there is a clear poor image but a strong product. relationship among brand awareness, consideration and purchase, and therefore, Conversely, Brand A’s image would appear market share. However, we also know not all to be better than its product, given the brands are equal, and some are more proportion of people who consider but successful converting awareness into don’t purchase. Both companies could consideration and some at converting diagnose their perceptual aberrations consideration into trial. through corporate image research. HEIRARCHY OF EFFECTS Awareness Interest Consideration Trial Evaluation Repeat 1 The results from corporate image research Decompositional methods begin with data can take many forms. For instance, that represent similarities between brands. The consider the above: data can be collected through direct similarity This shows direct comparisons between questioning, techniques such as repertory products on specific product features or grid, or by other measures of association, descriptions (attributes). Researchers call such as correlations. Decompositional graphs that show the market’s perceptions methods deal primarily with brand-to-brand of companies such as this one perceptual relationships. The brand- to- brand relationships maps. This example represents a simple are mapped without regard to why two perceptual map, but during the past 50 years brands might be similar, which occasionally researchers have developed several techniques creates problems in interpretation. to produce maps reflecting buyers’ perceptions. Unlike decompositional methods which The more sophisticated maps show relationships begin with brand level similarities, between brands, between attributes, and compositional methods begin with between attributes and brands. Compared to measurements of brands on attributes1. the simple map above (which just shows the There are three primary types of relationship between brands on one attribute compositional methods: factor analysis, at a time), the sophisticated maps provide a discriminant analysis, and correspondence great deal of information. The rest of this analysis. Normally, the measurements used document will discuss only techniques that are interval scaled ratings2. Using holistically combine multiple brands and a compositional approach, brand-to-brand product attributes onto one map. relationships are shown as in There are two major classes of perceptual decompositional techniques, but unlike mapping algorithms. The two classes are decompositional methods, the attributes generally referred to as compositional and creating that similarity are directly observed decompositional methods. Decompositional rather than inferred. In discriminant analysis techniques have many names but they and correspondence analysis the attributes typically fall under an umbrella term of are also included in the resulting map. multi-dimensional scaling (or MDS). Decompositional methods were extremely popular in the 1950s and 1960s. Since that time, though, they have fallen out of favor as more advanced methods have become available. Decompositional methods are still fervently used by some. Decompositional and compositional methods will be discussed below in detail. First, a history of the development of the techniques will be reviewed. Following the historical perspective, the strengths and weaknesses of each method will be discussed. II. BACKGROUND AND HISTORY References of using factor analysis as a France before the middle 1980s. The 3 perceptual technique date back to the 1930s , technique is compositional in nature, but but the first wave of perceptual research would unlike the two compositional methods above likely be associated with multidimensional (factor and discriminant), correspondence scaling. In the early 1960s, researchers sought analysis does not require ratings data. an approach using ordinal (non-ratings) input Instead, correspondence analysis uses data to produce output with metric qualities. aggregate level counts. This approach (known as multidimensional scaling or MDS) was largely developed by III. MAPPING TECHNIQUES Bell Labs, especially by Shepard and Kruskal. Decompositional Multidimensional While elegant theoretically, the data Scaling (MDS) requirements and frequent problems MDS is really a broad name for a wide variety with interpretation created headaches of algorithms. At the heart of all of the methods, for many researchers. though, is a desire to produce a map in a low Coincidentally, factor analysis once again dimensional space (normally two dimensions) became vogue as a replacement to MDS. that shows similarities between products. Factor analysis is compositional in nature Some of the more common names of MDS (alleviating the interpretation problems) and algorithms include ALSCAL, INDSCAL, can use standard ratings data as input MDPREF, MDSCAL, ASCAL, KYST, and (eliminating the problems encountered PREFMAP. For the purposes of this exposition, collecting similarities data). Meyers and the dierences between these models Tauber attempt an explanation of its appeal are not important. in the 1970s. "One of the most obvious The data input requirements for MDS are alternatives [to MDS] was factor analysis, a generally not stringent. Most techniques use technique that was both widely understood aggregate data, while methods exist to and easily applied by most investigators.... utilize individual data. The data are Moreover, the output format was for all referred to as similarities data, but the practical purposes indistinguishable." popular computer programs are capable of At about the same time, the application of handling a number of types of input, such as another common multivariate method was correlations or distances. gaining support. Discriminant analysis was There are several ways to collect brand used to determine dierences between similarity data. The most straightforward is brands. This method was presented in the to ask respondents directly to rate how literature and popularized by Johnson. similar two brands are on a scale, where a "1" In the 1980s, researchers were introduced indicates two brands are identical, and a "9“ to yet another mapping method called Correspondence Analysis, sometimes referred to as Dual Scaling. The method of CA has actually been around since the 1950s, but had been used almost exclusively by academic researchers in South Africa and indicates that two brands dier widely. Many to the same uni-dimensional plot and respondents find this level of abstraction maintain the distance metric as outlined dicult to deal with, though. Repertory above. Seattle and Miami are still 34.5 units grid is a particularly useful technique for away, Kansas City and Seattle are now 20 developing similarities. In repertory grid, units away from each other (corresponding to respondents are presented with three the actual distance of 1994 miles) and Kansas products and asked to indicate which City and Miami are 14.5 units away from each is most unique (or alternatively which other (corresponding to the actual distance of two are most alike)4. 1516 miles). However, this simple approach falls apart if we try to add Los Angeles. LA is 1190 miles from Seattle, which would suggest a position near 12 on the scale above. LA is also 2817 miles from Miami, which would suggest a position near 7 on the scale above. We could split the dierence and position LA at approximately 10 on the map. Then we have a new problem. Kansas City and LA should be 17 units apart, based on the actual distance of 1728, but by placing LA at 10, they would only be 10 units apart. LA does not fit as neatly on The following simplified example of MDS will provide an understanding of the basic processes involved behind an MDS analysis. Let's look at highway distances between five U. S. cities: Seattle, Miami, Kansas City, Los Angeles, and New

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