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6 Conclusion

6.1. Examples of further applications


Two additional marketing issues that could usefully be modelled within the CDAP paradigm are industrial marketing and retail services.

In the case of industrial marketing, it is by no means uncommon for products made of different materials and with different engineering properties to satisfy the same user needs. A natural example is in materials. Steels and plastics have different properties relating to corrosion, heat resistance, expansion coefficients, weight, appearance and so on. Some of these attributes are clearly defined by the mathematics of physics and engineering while some such as appearance relate to customer perceptions. Marketing strategies could be geared to changing perceptions or making customers more aware of a largely unnoticed physical attribute or increasing the importance of one attribute for some purposes. CDAP models of a competitive set including some steels, other metals such as aluminium and some plastics could certainly be used to determine the consistency of the data and the suppliers' views of the reasons why customers and potential customers use the materials they do.

Retail services such as restaurants appeal to their customers very largely on the basis of image, atmosphere and similar attributes which amount to user perception rather than physical characteristics. There are, of course, as many different reasons for dining out as there are reasons for buying alcoholic beverages. As shopping increasingly becomes a leisure activity per se, then the image and atmosphere of other kinds of retail outlets will (or have) become similarly important. It is not hard to imagine relevant CDAP states for retail outlets. Functional (i.e., shopping only to acquire specified goods) and recreational CDAP states come immediately to mind. There could well be different recreational or functional purposes that are relevant in different environments.

The usefulness of CDAP analysis in these markets is a matter for further research. We only note here that they are opposite ends of the spectrum from the dominance of customer perceptions to the dominance of physical characteristics.


Artificially Intelligent Specification and Analysis of Context-Dependent Attribute Preferences - 03 NOV 97
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