Advancing Telecom Customer Churn using Deep Learning

In the telecommunication industry, customer churn is a significant challenge. The term represents a customer cancelling their subscription or switching to another service provider. This paper quantified the ability of alternative neural network designs to predict customer churn and solved the problem. The architectures used in this research architecture are Gated Recurrent Unit (GRU), Bidirectional Long ShortTerm Memory (BiLSTM), a hybrid of GRU with BiLSTM and a hybrid of GRU and LSTM. These models gave good results and they reached the highest accuracies using the GRU model at $\mathbf{9 2. 2 9 \%}$, the BiLSTM model at $\mathbf{8 9. 2 0 \%}$, the GRU-BiLSTM model at 91.79%, and the GRU-LSTM model at 95.20% respectively. Remarkably, the hybrid GRU-LSTM model achieved higher accuracy and less computation time compared to the other models. This points out that such hybrid models possess strength in combining different varieties of neural architectures; therefore, they may be able to represent better customer churn predictions. The findings of this study have monumental implications for the telecommunication industry. Advanced architectures of neural networks may predict customer churn more accurately so that service providers can act proactively with retention to improve a business outcome. This could potentially lead to improved customer retention and business success.