Deep Learning Based Customer Churn Analysis

Traditional customer churn is predicted by machine learning and data mining methods. The advantages of big data are not fully utilized. In this paper, we use deep learning based customer churn analysis to establish a predictive model of customer churn, to achieve a warning real time. We use stacked autoencoder network to extract features from the data and then use logistic regression (LR) to classify customers. We first pretrain stacked autoencoder network, which is a deep learning model that uses the greedy layerwise unsupervised learning algorithm to train. After pretraining each layer separately, we will stack the each layer to form stacked autoencoder network, using backpropagation (BP) algorithm to reverse tuning parameters, and then train the logistic regression layer. From the perspective of overall accuracy, we use accuracy to evaluate the model, if the company pays more attention to all the churner to be predicted, we use the recall to evaluate the model, if the company pays more attention to the predicted accuracy of the churner, we can use the precision to evaluate the model. As to the customers who predict the loss, change the shortages in the operation timely, and reduce the loss of customers.