Leveraging unstructured call log data for customer churn prediction

Customer retention is important in the financial services industry. Machine learning has been incorporated into customer data analytics to predict client churn risks. Despite its success, existing approaches primarily use only structured data, e.g., demographics and account history. Data mining with unstructured data, e.g., customer interaction, can reveal more insights, which has not been adequately leveraged. In this research, we propose a customer churn prediction model utilizing the unstructured data, which is the spoken contents in phone communication. We collected a large-scale call center dataset with two million calls from more than two hundred thousand customers and conducted extensive experiments. The results show that our model can accurately predict the client churn risks and generate meaningful insights using interpretable machine learning with personality traits and customer segments. We discuss how these insights can help managers develop retention strategies customized for different customer segments. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Customer retention is crucial in the financial services industry, and machine learning has been used to predict client churn risks. While existing approaches mainly rely on structured data, mining unstructured data can provide more insights. The research introduced a model utilizing spoken contents in phone communication for customer churn prediction, which showed promising results in accurately predicting risks and generating meaningful insights.