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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 3, march 2022
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 3, march 2022
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    Using Customer Knowledge Surveys to Explain Sales of Postgraduate Programs: A Machine Learning Approach

    Autor: 
    Asensio, Eva
    ;
    Almeida, Alejandro
    ;
    Galiano, Aida
    ;
    Martín-Álvarez, Juan-Manuel
    Fecha: 
    03/2022
    Palabra clave: 
    business intelligence; business analytics; business decision making; clustering; machine learning; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13135
    DOI: 
    https://doi.org/10.9781/ijimai.2022.01.008
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/3096
    Open Access
    Resumen:
    Universities collect information from each customer that contacts them through their websites and social media profiles. Customer knowledge surveys are the main information-gathering tool used to obtain this information about potential students. In this paper, we propose using the information gained via surveys along with enrolment databases, to group customers into homogeneous clusters in order to identify target customers who are more likely to enroll. The use of such a cluster strategy will increase the probability of converting contacts into customers and will allow the marketing and admission departments to focus on those customers with a greater probability of enrolling, thereby increasing efficiency. The specific characteristics of each cluster and those postgraduate programs that are more likely to be selected are identified. In addition, better insight into customers regarding their enrolment choices thanks to our cluster strategy, will allow universities to personalize their services resulting in greater satisfaction and, consequently, in increased future enrolment.
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