Making marketing strategies is a complex process requiring research, judgment and creativity. Perceptual mapping is a powerful tool for exploring data and generating hypotheses. This article discusses three types of perceptual maps: preference, multidimensional scaling (MDS) and correspondence.
Marketing research helps marketers establish an objective (or, simulated/virtual) marketplace to understand their customers and their products, or answering questions like: How do my customers use my product? What are the strengths and weakness of my product relative to my competition? Where does my product fit in the overall market consuming such products? Who are the targeted customers for my product?
Once this structured framework is established and understood, it then becomes a guide and analytic platform for creative strategists to design innovative, targeted strategies (to fill the gaps, or to raise the existing product to a higher ground, etc.).
Perceptual mapping is one of the many techniques used in the analytic steps, and an extremely popular one. Its beauty is in its graphical display: Simpler to interpret than a listing of numerical results, it quickly points to potential relationships, connections, and patterns in the data. Its deficiency is that the graph is only an approximate representation of the real data, because of the amount of data condensation/transformation the procedure requires. Therefore, perceptual mapping should not be used alone to reach any conclusions, and must be accompanied by other mathematical means to verify its findings. In general, perceptual mapping is a powerful tool for exploring data, and for coming up with hypotheses.
There are three ways of producing perceptual maps, although most people are familiar with only one: the MDS map. The three types of maps are produced by three different techniques and have different usages:
1. Preference map
2. Multidimensional scaling (MDS) map
3. Correspondence map
Each map requires a different view of the input data, and the maps are used to study different aspects of the marketing problem.
1. Preference map (for study of consumer preferences) A basic preference map shows consumers’ preferences for a set of products. It is more useful than presenting a table of mean ratings. In a typical preference analysis, consumers are surveyed for their preferences for a set of products. For example, 15 consumers are asked to rate their preferences for 10 U.S.-made cars on a rating from 1 to 10 (1 is the least preferred, 10 is the most preferred). Preference analysis performs a principal component analysis on the rating data, and then plots the first two principal components from the analysis to create an approximate two-dimensional display of the consumer preferences for the 10 cars.
2. Multidimensional scaling map (for analysis of product competitiveness) Multidimensional scaling is a graphic technique for analysing the similarities (or dissimilarities) between products. It is not meant for studying consumer preference, but for analysing competitive positioning of the products in the minds of the consumers. The data: For a multidimensional scaling survey, it would be ideal, but highly impractical, to ask every consumer to rate the degree of similarity (or dissimilarity) between all possible pairs of products, because the number of pairs of products to rate would be too large if there are many products. Alternatively, each consumer is asked to place the products into groups of similar products. Consumers can decide as many or as few groups as they like. Multidimensional scaling performs an initial principal component analysis of the original data, and then improves on the solution iteratively. When the solution can no longer be improved, the procedure stops and produces an optimal two-dimensional map of product distances.
3. Correspondence map (to explore information in any frequency table) Correspondence analysis is an ingenious device to explore the associative relationships and clustering patterns in the frequency data. For example, you can use the correspondence map to examine the association between a categorical variable that identifies a group of customers and another categorical variable that distinguishes your product. It is even equipped to display multiple categorical variables simultaneously (such as in multi-way tables of frequency), each having a large number of levels, although with some sacrifice (i.e., the distances between all points in the plot become meaningless).
Submitted By:
Amritha Shrikumar
( Roll No. -12069)
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