In this project, I used a public dataset from UCI in order to explore the benefits of an unsupervised machine learning technique. The main purpose of this project is to use a clustering technique in order to gain insights than can improve customer loyalty, sales, and profits.
RFM analysis (Recency, Frequency, Monetary), a proven marketing model for customer segmentation, was used to elaborate the input variables for clustering.
Some of the different techniques used to find a optimal number of clusters were: Elbow, Average Silhouette, and Gap Statistic methods.