Mining Twitter to Identify Customers’ Requirements and Shoe Market Segmentation

Shoe manufacturers suffer from developing new products due to the complexity and uncertainty in design stage. Thus, it is essentially required for them to identify customers' needs and wants as quickly as possible. However, lack of literatures and methods obstruct manufacturers from designing new product efficiently. This study aims at analyzing electronic word-of-mouth to provide meaningful suggestions for them. Specifically, we conducted keyword frequency and co-occurrence analyses by retrieving 50,456 unique keyword in 10,000 tweets from Twitter. We confirmed the principle of hedonic dominance and revealed shoe market can be segmented by six clusters. We also visualized relationships among keywords by using VOSviewer. Various theoretical and practical implications are discussed.