E-commerce provides a global platform supporting product transactions through the consumer purchase lifecycle including communications of perceived satisfaction and dissatisfaction. The customer feedback functions and social networks of many e-commerce websites allow for the creation of extremely large databases that can be mined to model the customers' perceptions toward online purchases. This research uses online customer reviews as the business intelligence corpus to help companies redesign products that better satisfy consumer preferences and differentiate their product offerings. After identifying the specific webpages of customer reviews, a web crawler collects review text. Computer-supported text mining, cluster analysis, and perceptual mapping are combined as a systematic analytic approach to compare products in a given domain. The study assists phone manufacturers to understand the positive and negative perceptions of customers related to their post-purchase experiences. The customer-preferred product functions, features, and price positions provide valuable strategic intelligence for new product designs and market differentiation.
|Number of pages||17|
|Journal||International Journal on Semantic Web and Information Systems|
|State||Published - 2020|
Chang, A. C., Trappey, C., Trappey, A. J. C., & Chen, W. L. (2020). Web mining customer perceptions to define product positions and design preferences. International Journal on Semantic Web and Information Systems, 16(2), 42-58. . https://doi.org/10.4018/IJSWIS.2020040103