Telecom user churn prediction scheme based on large language model

With the fierce competition among major telecom operators for existing users, machine learning has made remarkable progress in customer churn prediction. However, most existing work and experiments rely on the data of local telecom operators, resulting in significant differences in algorithm selection and experimental results. Large language models(LLMs) have obtained surprising abilities in solving complex tasks through learning on large-scale unlabeled corpora and continuously scaling model size. By converting user data into a natural language format, LLM is employed to capture underlying patterns in the data and predict the probability of user churn in the following month. With extensive training on abundant text data from large-scale public corpora, the integration of LLMs in this task has the potential to serve as a general scheme and demonstrate unique advantages in few-shot scenarios and interpretability. A series of experiments are performed to test the capabilities and limitations of LLMs in customer churn prediction. The results indicate that with a certain number of prompts and instructions, LLM can perform well in this task. However, due to the limited rate of interaction with the model, it is necessary to initially employ some methods upstream of the model to screen potential churn users.