Online Brand Community User Segments: A Text Mining Approach - Ge, Ruichen; Zhao, Hong; Zhang, Sha (Frontiers in Artificial Intelligence, 2022)
There is a trend that customers increasingly join the online brand community. However, evidence shows that there are nuances between different user segments, and only a small group of users are active. Thus, one key concern marketers face is identifying and targeting specific segments and decreasing user churn rates in an online and mobile environment. This study aims to segment online brand community users and identify different characteristics of each segment. To this end, we propose a UGC-based segmentation of users in the brand community. We used python to obtain users’ post data from a well-known online brand community in China between July 2012 to December 2019, resulting in 912,452 posts and 20,492 users. We then use text mining and clustering methods to segment the users and compare the differences between the segments. Three groups - entertainment-oriented users, information-oriented users, and multi-motivation users - were identified. Our results imply that entertainment-oriented users were most active, yet, multi-directional users have the lowest probability of churning, with a churn rate of only 0.415 times than that of users who focus on a single type of content. Implications for marketing and future research opportunities are discussed.