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).
Two basic questions facing the researcher when dealing with backwards segmentation relate to the choice of the data handling technique and the choice of variables to work with. The findings reported here suggest that the latter question is far more crucial than the first. Whereas choice of technique to a large extent may be a question of the type of data available (metric/nonmetric, etc.) the choice of variables to work with turns out to be highly critical. For that reason it may be advisable to put more time into the latter question. Another conclusion established in the studies reported here is that backwards segmentation is useful in the sense that it makes it possible to identify segments, which differ in relation to the product studied. Moreover in the two studies reported here (and a couple of others not described) it seems that the segments which emerge normally will consist of one totally positive and a number of segments being negative for different reasons. Finally, a somewhat negative--but not unexpected--conclusion can be mentioned. Seemingly the segments thus identified do not differ largely in terms of more traditional variables such as income, age, sex. etc.
The technology we are going to describe has been developed as a composite procedure, which retains flexibility, whilst solving the problems of interlocking complex interviewing and analysis systems. We have named this composite system, The Consumer Oriented Grid Group Interview (COGGI) technique. We present it here as a relatively cheap and fast method of obtaining marketing strategies based on a type of consumer segmentation.