Tracking customer portrait by unsupervised classification techniques
| Authors | |
|---|---|
| Year of publication | 2012 |
| Type | Article in Periodical |
| Magazine / Source | Transformations in Business & Economics. Kaunas Faculty of Humanitie |
| MU Faculty or unit | |
| Citation | |
| Web | http://www.transformations.khf.vu.lt |
| Field | Informatics |
| Keywords | customer relationship management; CRM indicators; neural network analysis; sensitivity analysis; cluster analysis |
| Description | 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 analysis results were summarized and the research insights presented by analyzing 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. |
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