Churn prediction using time series data
Abstract: Customer churn is problematic for any business trying to expand their customer base. The acquisition of new customers to replace churned ones are associated with additional costs, whereas taking measures to retain existing customers may prove more cost efficient. As such, it is of interest to estimate the time until the occurrence of a potential churn for every customer in order to take preventive measures. The application of deep learning and machine learning to this type of problem using time series data is relatively new and there is a lot of recent research on this topic. This thesis is based on the assumption that early signs of churn can be detected by the temporal changes in customer behavior. Recurrent neural networks and more specifically long short-term memory (LSTM) and gated recurrent unit (GRU) are suitable contenders since they are designed to take the sequential time aspect of the data into account. Random forest (RF) and stochastic vector machine (SVM) are machine learning models that are frequently used in related research. The problem is solved through a classification approach, and a comparison is done with implementations using LSTM, GRU, RF, and SVM. According to the results, LSTM and GRU perform similarly while being slightly better than RF and SVM in the task of predicting customers that will churn in the coming six months, and that all models could potentially lead to cost savings according to simulations (using non-official but reasonable costs assigned to each prediction outcome). Predicting the time until churn is a more difficult problem and none of the models can give reliable estimates, but all models are significantly better than random predictions.
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