A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans
Mobile Network Operators (MNOs) present wireless services of the same kind in identical zones, clients select the service taking into account any element they consider relevant. Churning hits on the design of the network and the method to assign prices by MNOs, and of course their earnings. Therefor...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/113347 |
| Acceso en línea: | https://hdl.handle.net/2117/113347 https://dx.doi.org/10.1016/j.physa.2017.12.121 |
| Access Level: | acceso abierto |
| Palabra clave: | Social networks Social network Homophily Customer’s profile Churn customer model Agent-based model Xarxes socials Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| Sumario: | Mobile Network Operators (MNOs) present wireless services of the same kind in identical zones, clients select the service taking into account any element they consider relevant. Churning hits on the design of the network and the method to assign prices by MNOs, and of course their earnings. Therefore, MNOs try to reduce churn detecting potential churners before they leave the service. Our approach to churn prediction considers each customer individually. Previous research shows that members of the social circle of a subscriber may influence churn. Thus, many scenarios that describe social relations, and in which churning processes could be expected, set an emerging challenge with practical implications. This paper uses the Agent-Based Modelling (ABM) technique to model customers. The model’s parameters include demographic and psychographic features as well as usage profiles according to their social behavior considering their customers’ profiles. Our model modifies and extends an existing real social network generator algorithm that considers customer’s profiles and homophily considerations to create connections. We show that using our approach, groups of customers with greater tendency to churn due to the influence of their social networks can be identified better. |
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