An increasingly common practice among retailers is the creation of their own brands. The so-called private labels allow them to compete on price to attract new customers. The goal is to offer a quality similar to that of the traditional brands but at a lower price range. In this research, clustering algorithms based on machine learning techniques are applied to carry out a behavioral customer segmentation for private labels.

The research has been completed in four stages: analysis, design, development, and discussion. During the analysis, 1.073 customer loyalty surveys are preprocessed and analyzed. During the design, 23 questions are selected to design experiments. The clustering algorithms used in the investigation are Simple K-Means Algorithm (SKMA) and Expectation-Maximization Clustering (EMC).

Experimental results reveal clear differences in the performance of the selected algorithms. For example, when 4 clusters are predefined, SKMA distributes the instances according to the following proportions: 43%, 26%, 15%, and 15%. EMC, instead, distributes the instances in 18%, 23%, 31%, and 29%.

In conclusion, the results show that both algorithms, SKMA and EMC, are effective and useful to segment customers based on their preferences. However, the peculiarities of their clustering strategies gives rise to significantly different clusters.