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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F15%3APU114559" target="_blank" >RIV/00216305:26510/15:PU114559 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84947601807&origin=inward" target="_blank" >https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84947601807&origin=inward</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50602 - Public administration

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-0-9860419-4-5

ISSN

—

e-ISSN

—

Počet stran výsledku

11

Strana od-do

2561-2571

Název nakladatele

Neuveden

Místo vydání

Amsterdam, Netherlands

Místo konání akce

Amsterdam

Datum konání akce

7. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000360508700250