Sequential Sentiment Pattern Mining to Predict Churn in CRM Systems: A Case Study with Telecom Data

Abstract

Competitive markets have driven companies to engage in customer retention. Therefore, anticipating client churn (i.e., cancellation) became a significant challenge. Sequential pattern detection is one of the most popular approaches to the churn prediction problem. Although promising, initiatives based on this approach do not take into account information that can be useful to detect churn: the underlying sentiment in the interactions between client and company. This study raises the hypothesis that considering such information may improve churn prediction, since they may indicate the client's satisfaction level with services and products. Hence, the present study aims to evaluate the validity of the formulated hypothesis. For this purpose, it applies a method that generates churn prediction models from the combination of sequential pattern detection with sentiment extraction from customer-company interactions. Experimental results from telecom data confirm the adequacy of the generated models and of the raised hypothesis.