Backwards segmentation using hierarchical clustering and Q-factor analysis
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.
- This could also be of interest