Impact of natural language processing on CRM and trust: An integrated framework

Abstract

Natural language processing (NLP) is an interdisciplinary concept with anecdotal studies in many fields. A limited number of studies have empirically investigated NLP applications in several industries. This research is intended to fill this gap in the literature. Using traditional tools to interact with customers in their natural languages creates misinterpretations and increases cost in language processing. This study proposes NLP techniques to minimize human factor errors in natural language processing and build multilevel relationship trust. Therefore, a sentiment analysis-based literature review is conducted to examine customer relationship management (CRM) empowered by NLP that offers a new way of building trust with customers effectively. More specifically, sentiment polarity was used to separate the favorable and unfavorable views of NLP applications. We found an integrated framework showing pillars of artificial intelligence such as machine learning, deep learning, and NLP. We also found that Data warehouse and Hadoop feed the framework which relates CRM to Trust. Future research can be used to test the framework using statistical inferences. Theoretical contributions and practical contributions are offered.