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Customer Satisfaction Measurement – Clustering Approach

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F18%3APU127810" target="_blank" >RIV/00216305:26510/18:PU127810 - isvavai.cz</a>

  • Result on the web

    <a href="https://acta.mendelu.cz/66/2/0561/" target="_blank" >https://acta.mendelu.cz/66/2/0561/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.11118/actaun201866020561" target="_blank" >10.11118/actaun201866020561</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Customer Satisfaction Measurement – Clustering Approach

  • Original language description

    The paper deals with the issue of customer satisfaction measurement. The aim of this study is to determine the importance of the individual factors and their impact on total customer satisfaction for multiple segments by using linear regression and hierarchical clustering. This study is focused on the market of café establishment. We applied hierarchical clustering with Ward’s criterion to partition customers into segments and then we developed linear regression models for each segment. Linear models for partitioned data showed higher coefficient of determination than the model for the whole market. The results revealed that there are quite significant differences in rankings of customer satisfaction factors among the segments. This is caused by the different preferences of customers. The clustered data allows to achieve a higher homogeneity of data within the segment, which is crucial both for marketing theory and practice. The approach i.e. partitioning the market into smaller more specific segments could become perspective for marketing use in different economic sectors. This attitude can allow marketers to target better on customer segments according to the importance of individual factors.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    50204 - Business and management

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

  • ISSN

    1211-8516

  • e-ISSN

    2464-8310

  • Volume of the periodical

    66

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    9

  • Pages from-to

    561-569

  • UT code for WoS article

  • EID of the result in the Scopus database

    2-s2.0-85047602602