Segmenting Customers with Data Analytics Tools: Understanding and Engaging Target Audiences

Abstract

This paper presents a decision support system for identifying customer typology using cluster analysis to segment relevant customers. The approach is demonstrated using data from a company selling nutritional supplements, consisting of approximately 130,000 records from six Central European countries. The analysis results in distinct groups of customers, which are proposed for more effective management of customer relationships. The findings have implications for retailers, helping them focus on the most profitable customer segments to increase sales and profits and build lasting relationships. Furthermore, cluster analysis proves to be an appropriate statistical method for classification and provides valuable insights into patterns and trends in the analysed data. Overall, this paper contributes to development and comparison of methods for customer segmentation and demonstrates their potential for improving economic efficiency and building long-term customer relationships.

Keywords

• cluster analysis
• consumer behaviour
• two-step analysis
• mixed data
• customer segmentation