DATA-DRIVEN CUSTOMER SEGMENTATION BASED ON ONLINE REVIEW ANALYSIS AND CUSTOMER NETWORK CONSTRUCTION

Recently, many studies on product design have utilized online data for customer analysis. However, most of them treat online customers as a group of people with the same preferences while customer segmentation is a key strategy in conventional market analysis. To supplement this gap, this paper proposes a new methodology for online customer segmentation. First, customer attributes are extracted from online customer reviews. Then, a customer network is constructed based on the extracted attributes. Finally, the network is partitioned by modularity clustering and the resulting clusters are analyzed by topic frequency. The methodology is implemented to a smartphone review data. The result shows that online customers have different preferences as offline customers do, and they can be divided into separate groups with different tendencies for product features. This can help product designers to draw segment-based design implications from online data.