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利用網路資料探勘技術與知覺地圖協助產品定位與設計:以ASUS智慧型手機為案例
Thesis

利用網路資料探勘技術與知覺地圖協助產品定位與設計:以ASUS智慧型手機為案例

陳雯琳
Masters, 國立清華大學, 工業工程與工程管理學系
2015

Abstract

網路爬蟲 網路資料探勘 分群分析 關聯規則探勘 知覺地圖 市場定位 產品差異比較 Web Crawling Web Mining Clustering Analysis Association Rule Mining Perceptual Map Market Positioning Products Comparison
With the rapid development of e-commerce applications, online shopping has become more popular and convenient than in-store shopping. E-commerce provides a fast and global platform in which transactions, pre- and post-sales communications take place efficiently and rapidly. Online shopping offers a range of product options with no geographical limitations to customers. Many people choose to use the Internet to search and purchase products and, thus, Internet provides enormous opportunities to consumer goods marketers and manufacturers. 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 impressions of customers for 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 needs to collect customer review text. Afterwards, computer-assisted text mining, clustering analysis, association rule mining, 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.

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