One-to-one marketing management via customer complaint

Abstract

For companies to retain customers and ensure effective management-level resolution, they need to anticipate customer churn and determine the root cause of complaints. To achieve this, analyzing personalized complaints from the customer's perspective is crucial. This research advocates for a multidisciplinary approach that combines language behavior, relevance feature extraction, feature weighting, and sentiment analysis to extract the underlying problem in real-time. Applying this approach to the CFPB database sample yielded an accuracy rate of 82% and a system validity of 75%, which can help improve customer service and protect consumers in the financial and other service industries. By addressing individual customer issues that cause dissatisfaction, businesses can enhance customer satisfaction and retention levels. Thus, by analyzing complaints from a personalized standpoint, companies can identify the root cause of the problem, improve their services, and establish stronger customer relationships.