Diagnostic Data Analysis of Unstructured Texts Using Natural Language Processing for the Identification and Mitigation of Causes of Customer Churn in Companies

Abstract

Customer loss results in revenue reduction, increased costs, and diminished reputation, making it more difficult to acquire new customers. By conducting an analysis of the data regarding the causes of customer churn, it is possible to develop strategies to mitigate them and protect revenues and resources. Understanding the underlying reasons allows companies to focus their efforts on specific areas that demand attention, thus contributing to customer retention and business sustainability. This article is based on the use of natural language processing techniques and diagnostic analysis applied to textual records of simulated data related to reasons for customer churn, aiming to identify and understand them, as well as to propose solutions and strategies to prevent future cancellations.