Clustering is a machine learning technique to analyze data and to discover groups that share some similarities or closeness. It is useful for marketing segmentations because it allows classifying customers into groups based on certain characteristics. In literature, the most commonly studies segmentation types are: geographic, demographic, psychographic, behavioristic, volume, product-space, and benefit segmentation. This research is focused on behavioristic segmentations for supermarket chains.

Behavior patterns are the core of the behavioristic segmentation. It considers customers’ attitude toward brands, the knowledge of brands, purchasing habits and frequency. The segmentation related to loyalty is crucial to identify loyal customers and to focus marketing strategies and tactics. The revenue depends greatly on that segment.

The research has been carried in four stages: analysis, design, development, and discussion. During the analysis, 1.073 customer loyalty surveys are preprocessed and analyzed. Two algorithms are employed during the investigation: Simple K-Means Algorithm (SKMA) and Expectation-Maximization Clustering (EMC).

While SKMA cluster sizes are 27%, 15%, 15%, 26%, and 17%, EMC cluster sizes are 2%, 29%, 24%, 32%, and 13%. In conclusion, both SKMA and EMC help segmenting supermarket customers based on their behavior. However, behavioral segmentation requires a deeper analysis since the cluster boundaries are not evident.