A Study on Mobile Customer Churn Based on Learning from Soft Label Proportions

In real-life scenarios, manual labeling is costly and inefficient. besides sometimes there is privacy restrictions on the user’s information, which makes it difficult to detect customers losses in the mobile customer field. We cannot directly obtain information about a user is lost or not. However, the business manager can estimate the proportion of lost users based on his business experience. Besides, considering that the proportion information we obtain in reality is estimated and not completely accurate, we propose a soft label proportional learning method based on the learning from label proportions (LLP). In detail, we modify proportional loss term in the loss function by introducing a slack variable. In this way, the proportional loss within a certain range can be regarded as zero when the estimated proportional information is uncertain. The proportional information can be determined in a range by adjusting the parameter. The experimental results show the effectiveness of the proposed method, which provides a new way to effectively solve the problem of customer churn analysis in the field of mobile communication.