A Decision Support System for Customer Behavior Analysis Through Social Media Data Mining
Publication Name
2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024
Abstract
Elevating product quality is the most straightforward way to raise customer satisfaction. The most effective solution to investigate necessary points of improvement is to explore customers' feedback and complaints. Due to the growing use of social media channels for sharing ideas and reviews about products and services, the current study focused on social media data analysis to investigate products' frequent failures and forecast customer behavior using machine learning techniques. In this regard, warranty services are complementary services to raise the customer's satisfaction using a product-service approach. The study will support the warranty service providers to be prepared for flaws before a claim on a warranty occurs. We used the QFD house to prioritise the most defective components of the product. We validated the model using the claimed data of a laptop gathered by a LENOVO warranty service and compared them with the data gained from social media. Regarding the results, this paper verifies the role of social media data in revealing the most defective components and the prediction of customer behavior.
Open Access Status
This publication is not available as open access
First Page
70
Last Page
74