Tracking customer portrait by unsupervised classification techniques

Varování

Publikace nespadá pod Fakultu sportovních studií, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
Autoři

PITNER Tomáš KRIKSCIUNIENE Dalia SAKALAUSKAS Virgilijus

Rok publikování 2012
Druh Článek v odborném periodiku
Časopis / Zdroj Transformations in Business & Economics. Kaunas Faculty of Humanitie
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://www.transformations.khf.vu.lt
Obor Informatika
Klíčová slova customer relationship management; CRM indicators; neural network analysis; sensitivity analysis; cluster analysis
Popis 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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.

Další info