This paper introduces and reviews the concept of consumer cognitive styles as a new basis for segmenting markets. Using empirical data from members of the National Family Opinion Mail Panel, the authors show that consumers varying in cognitive differentiation and tolerance of ambiguity exhibit marked differences in their life styles i.e. in their interests, attitudes, opinions as well as media habits and preferred activities. These findings have significant implications for theoretical and applied research on consumer behavioural and cognitive differences.
This paper introduces and reviews the concept of consumer cognitive styles as a new basis for segmenting markets. Using empirical data from members of the National Family Opinion Mail Panel, the authors show that consumers varying in cognitive differentiation and tolerance of ambiguity exhibit marked differences in their life styles i.e. in their interests, attitudes, opinions as well as media habits and preferred activities. These findings have significant implications for theoretical and applied research on consumer behavioural and cognitive differences.
In the broadest sense of the term, market segmentation as a strategy has been with us as long as marketing itself. Essentially, it hinges on the principle that consumers are not all alike, and utilises that principle in the sale of merchandise. Until about ten years ago, segmentation research was based largely upon simple cross classification by demographic characteristics or purchase behaviour. During the 1960's, however, major research interest and emphasis has focussed upon new, more powerful, and hopefully more meaningful ways of partitioning markets based upon attitudes held by consumers.
The aim of this contribution is the evaluation of clustering techniques from the viewpoint of practical marketing application. Clustering techniques (segmentation methods, grouping methods) are procedures that search for and detect natural groupings of objects (eg. persons) which are described by their values on variables. The requirements that must be met by such methods and the criteria for their evaluation which will be discussed have been shown to be important in the repeated application to practical marketing problems.
The objective of this study was to evaluate the images of the new American subcompacts - Pinto, Vega, Gremlin - as perceived by a cross-section of college students and newlyweds. These images were compared and contrasted with those of the more popular imported competitors - Volkswagen Super Beetle, Datsun 1600, Toyota Corolla and Opel Kadett. Ford's Maverick, a true compact. was also included in the study in order to evaluate perceptual differences between the Maverick and the Pinto.
Our subject refers to a partial field in preparatory work for marketing decisions in the communication industry - that is one way of handling the available data. We shall not treat analytical techniques as forecasting or decision processing which allow particular answers to questions of the marketing practitioners. We only want to discuss one phase in which the researcher tries to explore the structure of the respective market.
Classification is a core issue in market segmentation. Through the use of relevant criteria, manufacturers are enabled to assess whether or not particular markets are better treated as homogeneous or, rather, as composed of a heterogeneous collection of subgroups, each differentially responsive to alternative product and promotional strategies. Classification criteria can also guide the formulation of these strategies and help in the assessment of their success or failure. This paper provides an overview of the current situation in the area. In doing so it outlines some general issues involved in classifying consumers, discusses some commonly used criteria and indicates possible new developments.
In the type of segmentation referred to in this paper, which is generally termed 'cluster analysis', we analyse the data by a number of characteristics simultaneously, instead of sequentially as described above. The type of cluster analysis described in this paper can be considered as a multi-dimensional extension of this type of procedure. Given a number of variables, the computer programme allocates each individual in the sample to one of a pre-determined number of clusters in such a way as to minimise the variance within clusters (or conversely, to maximise the proportion of total variance which is explained by the clustering process).