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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

SGEM INTERNATIONAL MULTIDISCIPLINARY SCIENTIFIC CONFERENCE ON SOCIAL sCIENCES AND ARTS

ISBN

978-619-7408-65-2

ISSN

2367-5659

e-ISSN

—

Počet stran výsledku

8

Strana od-do

233-240

Název nakladatele

STEF92 Ltd.

Místo vydání

Albena Bulgaria

Místo konání akce

Albena

Datum konání akce

26. 8. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—