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Predicting customer churn in telecommunications: A machine learning approach using tensor representations
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Autores
Orientador(es)
Resumo(s)
Customer churn has become an increasingly important concern in the telecommunications
sector, where high operational costs and market competition emphasize the need to retain
customers. This study aimed to evaluate if advanced methods can improve the identification
of customers at risk of churning. Conventional machine learning models revealed limitations
in recognizing churners, mainly due to class imbalance, despite achieving a good overall
accuracy. To address these challenges, a new method leveraging covariance structures is
presented, capturing complex relationships and patterns. In addition, the use of sentiment
analysis on customer interactions was explored, highlighting its potential for early churn
prediction. While churn prediction remains a difficult task influenced by evolving customer
behaviours, this analysis demonstrated that supervised machine learning along with proper
feature engineering can enhance churn detection, enabling telecommunication companies to
act proactively to improve retention, reduce customer losses and maintain income stability.
This work contributes to the adoption of data-driven strategies in customer management,
supporting telecommunications companies in addressing churn challenges with greater
efficiency.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Churn Prediction Telecommunications Imbalanced Data Powers of the Covariance Matrices Sentiment Analysis SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 10 - Reduced inequalities SDG 11 - Sustainable cities and communities