Enhancing Customer Relationship Management With Hybrid Deep Learning And Natural Language Processing

Abstract

CRM plays a significant part in helping businesses achieve customer satisfaction and customer retention. On the other hand, traditional CRM faces challenges in maximizing the benefits from customer data, mainly customer reviews, which include customer feedback and service review data from social media. Such issues have created a need for advanced CRM systems capable of mining information from structured and unstructured data and making actionable insights from the process. This paper proposes a hybrid system combining DL and NLP features to enable CRM systems to better segment customers and predict customers' sentiments. The objective of the research is to design a system capable of handling large volumes of customer interaction data to maximize customer experience personalization. It has been proposed to use the feature extraction capabilities of Convolutional Neural Network (CNN) along with the customer behavior prediction capabilities of Recurrent Neural Networks (RNN). Furthermore, NLP techniques are employed to evaluate consumer sentiment on the basis of text-based information. The model is trained using a huge amount of data in terms of CRM and compared to other CRM models using different metrics such as accuracy, precision, recall, and F1-score. The study shows that the hybrid model works much better than the conventional models, with an accuracy rate of 92.5% for the prediction of consumer behavior and 95.3% for sentiment analysis. The findings clearly demonstrate the effectiveness of hybrid DL/NLP models when applied to CRM tools. It can be assumed that the integration of the two technologies in the field of CRM may lead to better outcomes in the future.