Transformations  in
Business & Economics

Tracking Customer Portrait by Unsupervised Classification Techniques
Dalia Kriksciuniene, Tomas Pitner, Virgilijus Sakalauskas

ABSTRACT. The problem of the research is targeted to exploring the customer-related information by analysing marketing indicators in order to substantiate the enterprise financial results.

The concept of dynamic customer portrait is introduced for creating analytical model. The suggested model explores the most influential variable sets for identifying customer clusters and basis for their membership. The computational methods of neural network, sensitivity analysis and self-organized maps for unsupervised classification were applied and verified by the experimental research.

The experimental research was performed by applying the suggested model for customer database of the travel agency. The results of the analysis were summarized and the research insights presented by analysing the effectiveness of the method in forecasting financial outcomes related to customer mapping and migrating between clusters over the dynamic development of the customer portrait indicators.

KEYWORDS: customer relationship management, CRM indicators, neural network analysis, sensitivity analysis, cluster analysis.

JEL classification: G12, G14, G17, E27.