5th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM 2018

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F18%3APU134348" target="_blank" >RIV/00216305:26510/18:PU134348 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.5593/sgemsocial2018/1.50" target="_blank" >http://dx.doi.org/10.5593/sgemsocial2018/1.50</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/sgemsocial2018/1.50" target="_blank" >10.5593/sgemsocial2018/1.50</a>

Result language

angličtina

Original language name

5th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM 2018

Original language description

This paper deals with the use of data-driven marketing as the important part of digital marketing. Data are valuable and necessary input for sellers as they are able to make decisions in many fields of digital marketing. This study is focused on the customer satisfaction modelling whereas the main aim is to find out models that optimally describe total customer satisfaction and to determine the importance of individual factors and discover their impact on the total satisfaction. The process of modelling is demonstrated in the field of free-time dance industry. The emphasis is placed on the course attendants rated their satisfaction in particular areas as well as their overall satisfaction with the dance courses. The authors employed regression analysis to measure the importance of individual factors on overall satisfaction to find out what factors should be focused on. To do that, two separate models were developed, both with a clustered and non-clustered version. The first model uses individual factors as inputs, the second works with semantically differentiated factor groups. Both models perform poorly in the non-clustered case. The first model significantly improves its explanatory power when we apply it to clustered data obtained by clustering process using Ward's criterion, for which the factors are ordered according to their significance. When customer clusters are compared to each other, the most important are course attendance, price and instructor expertise for each cluster respectively. These findings can be used as the input for customer relationship management module to design appropriate products for individual customer groups or to improve overall quality of product portfolio. Moreover the clustering method provides more precise data and should be an integral part of customer relationship management module so that marketers can take more efficient measures.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

50204 - Business and management

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Article name in the collection

SGEM INTERNATIONAL MULTIDISCIPLINARY SCIENTIFIC CONFERENCE ON SOCIAL sCIENCES AND ARTS

ISBN

978-619-7408-65-2

ISSN

2367-5659

e-ISSN

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Number of pages

8

Pages from-to

233-240

Publisher name

STEF92 Ltd.

Place of publication

Albena Bulgaria

Event location

Albena

Event date

Aug 26, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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