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This study introduces an innovative system designed to predict customer satisfaction scores through the integration of sentiment analysis of customer feedback alongside all related factors from a Customer Relationship Management (CRM) system.
The system implements the latest transformer models like BERT and RoBERTa then assess customer sentiment using an ensemble learning voting mechanism for accurate sentiment classification, and adaptive customer satisfaction rating.
The model generates baseline scores dynamically, based on factors like customer loyalty, and frequency of interactions with the firm, thus enhancing accuracy and relevance when assessing satisfaction. The system is also developed to utilize Turkish data optimizing usage in market shares for firms serving that user group.
Empirical results indicate that the ensemble learning approach significantly improves the accuracy of sentiment analysis and the reliability of satisfaction quantification. This resource provides additional contribution to the CRM literature by providing a credible and scalable mechanism to assess customer satisfaction to potentially be implemented in practice across industries. Future work will focus on extending the system’s scalability and enhancing its predictive capabilities across diverse sectors.
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