Social network embeddings for churn prediction

• Autor(a) principal: Santos, Hugo Filipe Paulino dos
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/22051
• topic: Customer churn; Social network analysis; Network embeddings; Rotatividade de clientes; Análise de redes sociais; Representações latente de rede

With the large adoption of Internet customers became more aware of existing services and their prices. From the perspective of companies acquiring a new customer is more expensive than maintaining existing ones. In this sense, companies began to address the challenge of leaving customers to other companies. Customer churn is even more challenging in the telecommunications sector, because customers can change operator faster due to shorter loyalty period and easy migration service to other telecommunications operators without associated costs. Anticipating churn is therefore a major concern for telecommunication companies, which leads them to carry out retention campaigns for these customers. Predictive models allows us to predict whether a customer will leave their operator using that client’s past information. The present work describes how a predictive model was build to predict the outflow of customers exploring customer relationships. Unlike other works, it uses a social network analysis that takes advantage of small customer representations (network embeddings) and allows to obtain better results than other methods.