Non-Contractual Churn Prediction with Limited User Information

Sammanfattning: This report compares the effectiveness of three statistical methods for predicting defecting viewers in SVT's video on demand (VOD) services: logistic regression, random forests, and long short-term memory recurrent neural networks (LSTMs). In particular, the report investigates whether or not sequential data consisting of users' weekly watch histories can be used with LSTMs to achieve better predictive performance than the two other methods. The study found that the best LSTM models did outperform the other methods in terms of precision, recall, F-measure and AUC – but not accuracy. Logistic regression and random forests offered comparable performance results. The models are however subject to several notable limitations, so further research is advised.