PERCEPTUAL MAPPING..!!!

It is a marketing research technique in which consumer's views about a product are traced or plotted on a chart. Perceptual mapping is one of the few marketing research techniques that provides direct input into the strategic marketing planning process. It allows senior marketing planners to take a broad view of the strengths and weaknesses of their product or service offerings relative to the strengths and weaknesses of their competition. Some other statistical analysis techniques used are Multiple regression, cluster analysis, structural equation modelling, data mining etc.

The map has the following characteristics:

·         Pair-wise distances between product alternatives directly indicate how close or far apart the products are in the minds of customers

·         A vector on the map indicates both magnitude and direction in the Euclidean space. Vectors are usually used to geometrically denote attributes of the perceptual maps

·         The axes of the map are a special set of vectors suggesting the underlying dimensions that best characterize how customers differentiate between alternatives

Uses of Perceptual maps:

·         Understand the competitive market structure as perceived by customers.

·         Represent customers’ perceptions in a manner that aids communication and discussion within the organization

·         Perceptions of a new product concept in the context of existing brands in the market

·         Finding the “gap” in the market to position the product.

Applications:

·         Helps in analysing companies in traditional manufacturing industries who are attempting to move from basic commodities to faster growing “value-added” products.

·         Helps in facilitating layouts and solving layout problems.

MDS

Techniques that help to construct Perceptual Maps are called Multidimensional Scaling and Factor Analysis. Multidimensional scaling (MDS) is a mathematical technique that helps implement this analysis of similarity perceptions with minimum information loss.

What are advantages/disadvantages of MDS ???

·         Allows you to map products and simultaneously infer attributes.

·         Better for softer attributes which we do not verbalize very well (feel, aesthetics)

·         Impractical when the number of products are large.

Applications of MDS:

Applications include scientific visualisation and data mining in fields such as cognitive science, information science, psychophysics, psychometrics, marketing and ecology. New applications arise in the scope of autonomous wireless nodes which populate a space or an area. MDS may apply as a real time enhanced approach to monitoring and managing such populations. Furthermore, MDS has been used extensively in geostatistics for modelling the spatial variability of the patterns of an image, by representing them as points in a lower-dimensional space.

MDS algorithms fall into a taxonomy, depending on the meaning of the input matrix:

Classical multidimensional scaling

also known as Torgerson Scaling or Torgerson–Gower scaling – takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain

Metric multidimensional scaling

A superset of classical MDS that generalizes the optimization procedure to a variety of loss functions and input matrices of known distances with weights and so on. A useful loss function in this context is called stress which is often minimized using a procedure called stress majorization.

Non-metric multidimensional scaling

In contrast to metric MDS, non-metric MDS finds both a non-parametric monotonic relationship between the dissimilarities in the item-item matrix and the Euclidean distances between items, and the location of each item in the low-dimensional space. The relationship is typically found using isotonic regression.

Louis Guttman's smallest space analysis (SSA) is an example of a non-metric MDS procedure.

Generalized multidimensional scaling

An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In case when the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.

Thus perceptual mapping has got ample number of applications in the industry and the application might vary from industry to industry.

 

By

Vaishnavi Kambham - operations group 1