Understanding customer behavior is one of the key elements in any thriving business. Dividing customers into different groups based on their distinct characteristics can help significantly when designing the service. Understanding the unique needs of customer groups is also the basis for modern marketing. The aim of this study is to explore what types of customer groups exist in an
entertainment service business.
In this study, customer segmentation is conducted with k-prototypes, a variation of k-means clustering. K-prototypes is a machine learning approach partitioning a group of observations into subgroups. These subgroups have little variation within the group and clear differences when compared to other subgroups. The advantage of k-prototypes is that it can process both categorical and numeric data efficiently.
The results show that there are significant and meaningful differences between customer groups emerging from k-prototypes clustering. These customer groups can be targeted based on their unique characteristics and their reactions to different types of marketing actions vary.
The unique characteristics of the customer groups can be utilized to target marketing actions better. Other possibilities to benefit from customer segmentation include such as personalized
views, recommendations and helping strategy level decision making when designing the service. Many of these require further technical development or deeper understanding of the segments. Data selection as well as the quality of the data has an impact on the results and those should be considered carefully when deciding future actions on customer segmentation.
