## 2 Materials and Methods  
Two situations shall be described and dealt with, one assuming that very little information about the market is available, the other providing, on the contrary, lots of market data which must be processed and automated correctly using suitable technologies. Since not much is known about individual customers in the first scenario, it is necessary to take a model-based approach that will try to estimate customers' behavioral habits. Once an estimate is available, product designs can subsequently be suggested that will draw on these habits. The models require some inputs as well, however, and in this text, the inputs are represented by the overall sales of the existing variants of an observed product, i.e., by information about the corresponding market as a whole. Accordingly, it is assumed for the model to be used that it is known which product variant has been the top seller, and if necessary, which variant ranked second in sales, third, etc. Further, it is assumed that it is known which product characteristics might generally be observed by customers for a given product and whether they are preferred to be minimized or maximized. Such general knowledge is usually available. As an example, it can certainly be said that when it comes to cars, for instance, customers observe engine power, maximal speed, and fuel consumption, among other things, and it is obvious whether higher levels of these features are perceived better or worse. On the other hand, it is much harder to recognize which of these characteristics are preferred more, to the detriment of others, and to what extent exactly for each individual customer. Thus, what is important in this information scenario is that more specific preferences of individual customers are outside companies' knowledge, and rather general information about the entire market is available only. If product characteristics are mutually independent, it is possible to use the Analytic Hierarchy Process (AHP) theory as a behavioral model [1], as is done in what follows. This would be the situation when customers are, for instance, so interested in technical features of a product that nothing else, including price, was of interest to them. In many instances, however, price will probably also be a considered feature, in which case a dependence will exist among the characteristics, since price is a function of other features. The case of dependent features is also commented on later in the text. The AHP model is a procedure that helps a decision-maker solve a multicriteria decision-making problem; that is, it helps find among decision-making variants the one that could best fit his/her needs, and although no mathematical model can fully embrace one's behavior, experience is such that decision-makers using the model often see in the outcome provided by the model a variant which is not far from the variant he or she would have intuitively selected had there been no model. Thus, the model can be applied to at least approximately identify the product variant among the considered variants that might be the most prioritized by the decision-maker; it will be the variant with the highest value assigned to it by the AHP model. The outcome returned by the model can be expected to be either the product variant that appeals the most, or at least a variant which is not far from the most appealing variant. The evaluation of an ith product variant by the AHPmodel is such that if the quantified level of its jth characteristic is \(a_{ij}\) and the importance or weight of the jth characteristic is \(w_{j}\in[0,1]\)--usually determined by a method of pairwise comparisons [1; 2]--the ith variant has an overall AHP-based value of \(\sum_{i}w_{j}\tilde{a}_{ij}\), where \(\tilde{a}_{ij}\) is the result of normalizing \(a_{ij}\): \(\tilde{a}_{ij}=a_{ij}/\sum_{i}a_{ij}\), if the jth feature is preferred to be maximized, or \(\tilde{a}_{ij}=\left(1/a_{ij}\right)/\sum_{i}\left(1/a_{ij}\right)\), if the jth characteristic is preferred to be minimized.