In layman terms, correspondence analysis is the depiction of some entities in a two or more dimensional perceptual map that reflects the similarities and differences between the entities. Consider for instance the set of bubbles shown on the left of Exhibit 1.12. These bubbles, which differ in size and shade, are represented on a two-dimensional map so that bubbles that are similar are placed next to one another, and those that differ are further apart.
Similarly, by employing statistical techniques like correspondence analysis, the brands and their image attributes can also be vividly depicted on a multidimensional perceptual map. Statistical packages like SPSS and SAS are used to craft perceptual maps of brands based on their image profile. The approach is summed up in the following steps:
These steps are outlined in Exhibit 1.13 for Organics and Pantene, and the resulting perceptual map is shown in Exhibit 1.14.
The following are guidelines on how to interpret perceptual maps:
With regard to the shampoo data, the following are some conclusions one can draw from the perceptual map:
Note that the attributes used in this shampoo example pertain primarily to functional, rational aspects such as makes hair shiny, prevents dandruff, strengthens roots; and that there is less use in the example of emotive, personality, or brand-consumer relationship benefits. The choice of attributes, as mentioned, is primarily dependent on the purpose and objectives of the study. Since Organics offers functional benefits, the brand’s manager wants to assess the brand’s image along functional attributes.
If, on the other hand, a marketer is seeking to understand the personality of a brand targeting a demographic profile such as young women, through personality and relationship traits, the attributes the marketer might consider using would pertain to emotive, personality and brand-consumer relationship benefits.
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