PITNER, Tomáš, Dalia KRIKSCIUNIENE a Virgilijus SAKALAUSKAS. Tracking customer portrait by unsupervised classification techniques. Transformations in Business & Economics. Kaunas Faculty of Humanitie. Vilnius, Litva: Vilnius University, 2012, roč. 11, č. 3, s. 167-189. ISSN 1648-4460. |
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@article{1074360, author = {Pitner, Tomáš and Kriksciuniene, Dalia and Sakalauskas, Virgilijus}, article_location = {Vilnius, Litva}, article_number = {3}, keywords = {customer relationship management; CRM indicators; neural network analysis; sensitivity analysis; cluster analysis}, language = {eng}, issn = {1648-4460}, journal = {Transformations in Business & Economics. Kaunas Faculty of Humanitie}, note = {Carried out during the tenure of an ERCIM "Alain Bensoussan" Fellowship Programme}, title = {Tracking customer portrait by unsupervised classification techniques}, url = {http://www.transformations.khf.vu.lt}, volume = {11}, year = {2012} }
TY - JOUR ID - 1074360 AU - Pitner, Tomáš - Kriksciuniene, Dalia - Sakalauskas, Virgilijus PY - 2012 TI - Tracking customer portrait by unsupervised classification techniques JF - Transformations in Business & Economics. Kaunas Faculty of Humanitie VL - 11 IS - 3 SP - 167-189 EP - 167-189 PB - Vilnius University SN - 16484460 N1 - Carried out during the tenure of an ERCIM "Alain Bensoussan" Fellowship Programme KW - customer relationship management KW - CRM indicators KW - neural network analysis KW - sensitivity analysis KW - cluster analysis UR - http://www.transformations.khf.vu.lt N2 - 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. ER -
PITNER, Tomáš, Dalia KRIKSCIUNIENE a Virgilijus SAKALAUSKAS. Tracking customer portrait by unsupervised classification techniques. \textit{Transformations in Business \&{} Economics. Kaunas Faculty of Humanitie}. Vilnius, Litva: Vilnius University, 2012, roč.~11, č.~3, s.~167-189. ISSN~1648-4460.
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