## 5 Discussion of the Obtained Customers Segmentation  
The formulated model has an application value. The significance of applications of the model and analyses is illustrated with the use of a comparison of the loyalty measures for various groups of customers. On the basis of the identified model that is based on neural networks, the most important segmentation criteria have been indicated. These are K2, S1, P4, P11, P19, and P21 variables. They have been selected in a manner, which allows for the remaining groups to differentiate customers with respect to all of the loyalty measures in the best possible way. As the k-averages algorithm requires an a priori assumption as to the number of clusters, the solutions for three, four, and five segments have been tested. The greatest differences in the values of the loyalty measures have been obtained for the solution covering four segments, which have been accepted as the final ones. Figure 8 presents segmentation of customers. On the basis of the results of cluster analysis that are presented in Figure 8, it may be concluded that each of the four segments has its own characteristics. The first segment covers persons above 40 years of age, who more often than the other groups buy used vehicles, assess the level of the quality of the information materials presenting the services that are offered by the auto repair shops at the low level, however they simultaneously assess the assistance that is offered by the workers of the auto repair shop at the high level. The second segment includes persons above 40 years of age, but they more often buy new vehicles. Moreover, these customers assess the quality of the promotional materials of the auto repair shop and the comprehensiveness of the offered services at the high level. In the third segment, there are persons above 50 years of age, who also buy new vehicles, but they differ from the third segment in respect to the assessment of the selected attributes of the quality of provided services in that way that they assess it much worse. Whereas, the last, the fourth segment is mainly composed of young persons. Table 2 presents the values of the quality measures for each segment. Figure 8: Segmentation of customers of the auto repair shops with the use of cluster analysis. Source: authors’ study. The analysis of data presented in Table 2 suggests that the particular segments significantly differ from each other in respect to the loyalty. It is especially visible in the case of L and NPS rates, in the case of which the difference between segment 1 and 4 amounts, respectively, to 20 p.p. and 37. Whereas, in the case of the SOW indicator, this difference is also high and it amounts to 9 p.p. In general, the results of the study show that taking into consideration the values of all three measures; the customers of segment 1 are the most loyal ones. In this segment, the values of all three measures are the highest and amount to: L = 80%, SOW = 53%, NPS = 0. Segment 1 mainly includes the persons above 40 years of age, who much more often buy used vehicles, they assess promotional materials of an auto repair shop at the low level, but the assistance provided by the workers of the auto repair show is highly valued. Customers from the second segment seem to be moderately loyal, as despite significantly lower values of the quantity-oriented (L = 66%) and value-oriented (SOW = 47%) measures, the level of behavioral measure is high (NPS = \(-\)5). Customers above 40 years of age are moderately loyal, but they buy new vehicles more often. Additionally, they assess the quality of promotional materials that are published by the auto repair shops high, which is similar to the comprehensiveness of the offered services. Finally, customers that are included in segment 3 and 4 are the least loyal.