Integrating NLP in the Business Decision Support System to Promote Customer Loyalty

• Published in: 2023 International Conference on Emerging Research in Computational Science (ICERCS) pp. 1 - 6
• Main Authors: Sherif, Noora Hani, Raad Ali, Rabei, Fahidhil, Eay, Haroon, Noor Hanoon, Hussam, Ragheed, Ibrahem, Marwa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07-12-2023
• Subjects: Business; Companies; Customer loyalty; Decision making; Decision Support system; Decision support systems; Forecasting; Index Terms: Natural language processing; Predictive models; Sentiment analysis; Simulation
• DOI: 10.1109/ICERCS57948.2023.10434114

Natural Language Processing combines the fields of linguistics, computer science, and mathematics. It has been used in a variety of contexts with outstanding results. In business, client devotion is measured by how often one organization's goods and offerings are chosen over those of its rivals. Customers with strong brand loyalty do not easily change their minds based on availability or pricing. Customers are more likely to make another purchase from a company they trust in the future. There is a significant impact on a customer's purchase decision because it depends on their friends' opinions of a company. Thus, consumer loyalty can increase the rate at which businesses acquire new clients. Companies struggle to satisfy customers' growing demands. Customers' expectations tend to change more rapidly. Consumers assess a company as "best-in-class" rather than where it was a year ago. An organization or business utilizes a Decision Support System (DSS) to help make decisions and plan activities.DSS take in vast amounts of data and extracts valuable information to solve problems and make decisions. By analyzing unstructured data from new sources and extracting information in the format needed for decision-making, Natural Language Processing (NLP) enhances decision-making abilities. NLP-DSS technology can process language-based data more quickly and tirelessly than people. Generally, businesses use NLP to gain a deeper understanding of customer needs through sentiment analysis, acquire valuable information from unstructured data, enhance communication, and boost productivity. In natural language processing, computers can communicate with humans in their language while automating other language-related tasks. Natural language processing breaks down the human language into fragments to investigate and understand word meanings and phrase structures in context. The simulation results show that the proposed NLP-DSS model has a higher company efficiency ratio (98.3%), decisions level (96.8%), consumer approval (97.7%), revenue (96.9%), forecasting (96.6%), and customer loyalty (98.9%) than the existing methods.