Abstract
In this paper, we explore the utility of sentiment analysis and text classification of voice of the customer (VOC) for improving churn prediction, which is a task to detect customers who are about to quit. Our work is motivated by the observation that the increase of customer satisfaction will reproduce churn and the customer satisfaction can be reflected in some degree by applying NLP techniques on VOC, the unstructured textual information which captures a view of customer’s attitude and feedbacks. To the best of our knowledge, this is the first work that introduces text classification of VOC to churn prediction task. Experiments show that adding VOC analysis into a conventional churn prediction model results in a significant increase in predictive performance.
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Wang, Y., Satake, K., Onishi, T., Masuichi, H. (2018). Customer Churn Prediction Using Sentiment Analysis and Text Classification of VOC. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_12
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DOI: https://doi.org/10.1007/978-3-319-77116-8_12
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