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Customer clustering using RFM analysis

In this study, customers' behaviors are determined by detecting natural clusterings using existing reservation and customer data. We also customize their services and sales strategies according to these behaviors. The basic characteristics that provide these existing heuristics have been extracted by the decision tree approach after the K-means is implemented. It is determined that these characteristics are customer's product acquisition channel, specific product preferences, reservation periods, seasonal preference, etc. The fact that these characteristics show significant changes in each clusters indicates that the solution is generally successful and that these characteristics are successfully selected. This work plays an important role in creating campaigns and product packages appropriate for these groups' characteristics.