Multi-attribute market segmentation for the major customers of an Iranian steel-making company using value proposition elements

The Mass market strategy does not have a big chance for success in a competitive market. Therefore, by segmenting a heterogeneous market into some smaller and homogenous markets in which customers have similar characteristics, it is expected that the resources can be more efficiently utilized for meeting the needs of customers. The purpose of this study  was mathematical modelling of market segmentation of an Iranian steel-making company using value proposition elements of the company for the customers. The model that mentioned above was used for the analysis of data related to six value proposition elements from 129 major customers of the company. This model was solved using the GAMS software and the number of optimized segments was 9. In this study, the results obtained were compared with those achieved by the conventional segmentation methods such as K-means and self-organized neural networks and two-step clustering. Further, for the validation of the mathematical model used, due to the multi-attribute nature of the segmentation done, discriminant analysis of research data was done after segmentation and the success percentage of the ranking rule was found to be 95.3 %.  Also, the similarity criterion was computed for each potential new customer to show which group the new customer belonged.