In recent years, many e-commerce websites provide consumer feedback functions and social networks, allowing customers to share their purchasing and usage experiences online. Companies collect and analyze information from customers' reviews through the platform to understand the customers impressions of the products they purchased. Online customer reviews has been widely regarded as an important source of information influencing customers buying decisions. In addition, online customer reviews help companies to redesign their products with key features that better positions to target customers in promising market sectors. This research uses online customer reviews as the business intelligence (BI) corpus. After determining the source webpage of customer reviews, a web crawler is needed to collect customer review text. Afterwards, computer-assisted text mining, clustering analysis, and perceptual mapping are applied to develop a formal methodology to compare similar products in a given domain. In this research, the consumer electronic sector is studied. Mobile phone customer reviews are web crawled, collected, mined, and analyzed. The study assists mobile phone manufacturers to understand the voice of customers in both positive and negative perspectives of post-purchasing experiences. The customer-preferred product functions, hardware/software/app features, and price positions, as key business intelligence, are derived for new product designs and market launches.