Detail publikace

Profitability of Customer Satisfaction Segments: Genetic Algorithm Method in Multidimensional Clustering

SCHÜLLER, D. PEKÁREK, J. DOSTÁL, P. CHLEBOVSKÝ, V.

Originální název

Profitability of Customer Satisfaction Segments: Genetic Algorithm Method in Multidimensional Clustering

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Abstract Due to limited recourses it is difficult for companies to reach a 100% satisfaction level of all customers in all measured factors. Therefore it is argued in this study that the profitability of customer segments is the key driver which companies should take into account in the improvement process of customer satisfaction. The study presents the use of multidimensional genetic algorithm clustering as the efficient method which allows to divide existing customers of a company into relatively homogenous segments according to their satisfaction with the selected factors and on the basis of the profitability of each segment to identify different strategies for the satisfaction improvement process. The suggested procedure is demonstrated on the real data from the field of tea products.

Klíčová slova

Satisfaction, Segmentation, Genetic Algorithm Clustering, Profitability

Autoři

SCHÜLLER, D.; PEKÁREK, J.; DOSTÁL, P.; CHLEBOVSKÝ, V.

Rok RIV

2015

Vydáno

7. 5. 2015

Místo

Amsterdam, Netherlands

ISBN

978-0-9860419-4-5

Kniha

In Innovation Vision 2020: From Regional Development Sustainability to Global Economic Growth

Edice

25

ISSN

NEUVEDENO

Strany od

2561

Strany do

2571

Strany počet

11

URL

BibTex

@inproceedings{BUT115125,
  author="David {Schüller} and Jan {Pekárek} and Petr {Dostál} and Vít {Chlebovský}",
  title="Profitability of Customer Satisfaction Segments: Genetic Algorithm Method in Multidimensional Clustering",
  booktitle="In Innovation Vision 2020: From Regional Development Sustainability to Global Economic Growth",
  year="2015",
  series="25",
  pages="2561--2571",
  address="Amsterdam, Netherlands",
  isbn="978-0-9860419-4-5",
  url="https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84947601807&origin=inward"
}