Customer segmentation using RFM analysis

Bachelor thesis (2020)

Authors

J.M. van Burg Electrical Engineering, Mathematics and Computer Science

Contributors

J. Cai Statistics - (mentor)

J.W. van der Woude Mathematical Physics - (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

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Published Date

06-07-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper is a research on the segmentation of customers. The clustering of customers is done based on the variables recency, frequency and monetary value. Such a clustering is called an RFM-model. The clustering is done using the K-means clustering method. To find the optimal number of clusters the following performance metrics are used: Elbow method, Silhouette Analysis, and Davies-Bouldin Index. The RFM-model is extended by introducing the loyalty variable. This model is called an RFML-model. Lastly, further clustering is done within one of the clusters.

Files

BEP_14.pdf

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