01826nas a2200253   4500000000100000000000100001008004100002260001200043653002600055653002300081653002900104653001500133653002100148100001600169700002200185700001700207700003300224245010800257856007900365300001100444490000600455520109700461022001401558       2022                            d  c03/202210aBusiness Intelligence10aBusiness Analytics10aBusiness Decision Making10aClustering10aMachine Learning1 aEva Asensio1 aAlejandro Almeida1 aAida Galiano1 aJuan-Manuel Martín-Álvarez00aUsing Customer Knowledge Surveys to Explain Sales of Postgraduate Programs: A Machine Learning Approach  uhttps://www.ijimai.org/journal/sites/default/files/2022-02/ijimai7_3_9.pdf  a96-1020 v73 aUniversities 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.  a1989-1660