Tracking customer portrait by unsupervised classification
techniques

J 2012

Tracking customer portrait by unsupervised classification techniques

PITNER, Tomáš, Dalia KRIKSCIUNIENE and Virgilijus SAKALAUSKAS

Basic information

Original name

Tracking customer portrait by unsupervised classification techniques

Authors

PITNER, Tomáš (203 Czech Republic, belonging to the institution), Dalia KRIKSCIUNIENE (440 Lithuania, guarantor, belonging to the institution) and Virgilijus SAKALAUSKAS (440 Lithuania)

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Lithuania

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 0.459

RIV identification code

RIV/00216224:14330/12:00062000

Organization unit

Faculty of Informatics

UT WoS

000311708800011

Keywords in English

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

Abstract

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.

Links

LA09016, research and development project

Name: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM) (Acronym: ERCIM)

Investor: Ministry of Education, Youth and Sports of the CR, Czech Republic membership in the European Research Consortium for Informatics and Mathematics

Citovat

PITNER, Tomáš, Dalia KRIKSCIUNIENE and Virgilijus SAKALAUSKAS. Tracking customer portrait by unsupervised classification techniques. Transformations in Business & Economics. Kaunas Faculty of Humanitie. Vilnius, Litva: Vilnius University, 2012, vol. 11, No 3, p. 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 and Virgilijus SAKALAUSKAS. Tracking customer portrait by unsupervised classification techniques. \textit{Transformations in Business \&{} Economics. Kaunas Faculty of Humanitie}. Vilnius, Litva: Vilnius University, 2012, vol.~11, No~3, p.~167-189. ISSN~1648-4460.

Detailed Information on Publication Record