Customers Segmentation in the Insurance Company (TIC) Dataset

Wafa Qadadeh · Sherief Abdallah

58 citations0/0 files storedProcedia Computer Science

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Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

Customers’ Segmentation is an important concept for designing marketing campaigns to improve businesses and increase revenue. Clustering algorithms can help marketing experts to achieve this goal. The rapid growth of high dimensional databases and data warehouses, such as Customer Relationship Management (CRM), stressed the need for advanced data analytics techniques. In this paper we investigate different data analytics algorithms, specifically K-Means and SOM, using the TIC CRM dataset. While K-Means has shown promising clustering results, SOM has outperformed in the sense of: speed, quality of clustering, and visualization. Also we discuss how both techniques segmentation analysis can be useful in studying customer’s interest. The purpose of this paper is to provide a proof of concept (based on a small publicity of data) of how big data analytics can be used in customer segmentation.

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References

2011Estimating customer future value of different customer segments based on adapted RFM model in retail banking context 2010A Two Phase Clustering Method for Intelligent Customer Segmentation

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