## 3 Results
The first two rows of the table, related to client 1, are: (Engine, Tire, Max.price, Car type, Transmission, Max. consumption, True consumption) = (120, 16, 35, S, M, 9, 8.2) and (150, 16, 35, S, M, 9, 9.2). Since client 1 demanded an engine of at least 110 kW, two configurations were available for them: the company also produces 120 kW and 150 kW engines when it comes to engine powers of at least 110 kW, hence the two rows for the client. The designing of an application that analyzes the data in Table S1, avoiding the pitfalls mentioned earlier, and displays the results in a neat form can be found, for instance, in [4; 5; 6]. Its full description is well beyond the scope of this text, but the steps necessary to achieve the visual results for the car example (Figures 2-4) are in Appendix B. The figures below show the analytical output for the presented car example, and the corresponding application can also be seen in animated action at the website address cited in the Supplementary Materials of the paper. The result, which in this case belongs to the category of bubble graphs equipped with predefined parameters, shows that if the emphasis is placed only on the car type, then three clients, contained in the reduced table, prefer a sedan, another two an SUV, and one client would rather buy a hatchback (Figure 2). Thus, if the company wants to manufacture a single car type, then it should be a sedan. Similarly, Figure 3 shows that if the interest is focused only on the engine power, the most preferred version is that of 90 kW (three clients). The features determining the number of clients with shared preferences are selected via a tool seen on the right-hand side of the  
Figure 3: Result of the _properly_ implemented procedure described on page 5: number of clients having the same preference for an engine type. Only one client prefers the 150 kW engine, two clients prefer the 100 kW or 120 kW engine, and three clients want the 90 kW engine. Thus, if the car maker wants to know what two different engine powers it should focus on in its production, it should be the 90 kW and 120 kW combination, or the 90 kW and 100 kW combination. Figure 2: Result of the _properly_ implemented procedure described on page 5: number of clients having the same preference for a given car type. Only one client prefers the car type H (hatchback), three clients desire the S type (sedan), and two clients require the car type SUV. The result obviously implies that if the car maker wants to know what the best car type for production is, sedan is the answer because the largest part of the six-client market sample prefers a sedan. figures. Figure 4 shows how many clients prefer different combinations of car type and engine power. Since each combination of these two features is preferred by a single client, it is not clear from this elementary example working only with a handful of clients which combination should be manufactured. As mentioned earlier, the frequencies may be low, and so the user of the system should be given an option to select with respect to which independent product characteristics the frequencies should be calculated. This is ensured by the parameters on the right. For instance, if the user chooses only the engine power as the feature, the output should display how many clients prefer different engine powers. The engine power is certainly the kind of feature that is determined before the production and cannot be modified after the production. With tire size, the situation is different. Its choice can be made after the production. The focus on a reduced number of product features will allow the application to find more clients who share the same preferences regarding the important features.