Publication detail

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

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

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

Satisfaction, Segmentation, Genetic Algorithm Clustering, Profitability

RIV year

2015

Released

07.05.2015

Location

Amsterdam, Netherlands

ISBN

978-0-9860419-4-5

Book

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

Edition

25

Pages from

2561

Pages to

2571

Pages count

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",
  annote="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. 
",
  booktitle="In Innovation Vision 2020: From Regional Development Sustainability to Global Economic Growth",
  chapter="115125",
  edition="25",
  howpublished="online",
  year="2015",
  month="may",
  pages="2561--2571",
  type="conference paper"
}