Tracking customer portrait by unsupervised classification
techniques

J 2012

Tracking customer portrait by unsupervised classification techniques

PITNER, Tomáš, Dalia KRIKSCIUNIENE a Virgilijus SAKALAUSKAS

Základní údaje

Originální název

Tracking customer portrait by unsupervised classification techniques

Autoři

PITNER, Tomáš (203 Česká republika, domácí), Dalia KRIKSCIUNIENE (440 Litva, garant, domácí) a Virgilijus SAKALAUSKAS (440 Litva)

Vydání

Transformations in Business & Economics. Kaunas Faculty of Humanitie, Vilnius, Litva, Vilnius University, 2012, 1648-4460

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Litva

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 0.459

Kód RIV

RIV/00216224:14330/12:00062000

Organizační jednotka

Fakulta informatiky

UT WoS

000311708800011

Klíčová slova anglicky

customer relationship management; CRM indicators; neural network analysis; sensitivity analysis; cluster analysis

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 4. 2013 15:48, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

LA09016, projekt VaV

Název: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM) (Akronym: ERCIM)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Účast ČR v European Research Consortium for Informatics and Mathematics

Citovat

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.

@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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