With the fast development of online social media, social network services have become an important research area nowadays. We are now in the era of social colonization, in which technologies such as Facebook Connect and Google Friend Connect have standardized social functionalities among a vast majority of websites. Particularly, microblog as a new star needs more attention. Although most of current studies have focused on the effect of social network on the diffusion of services or information, usually those studies are descriptions or explanations of what already has happened. Limited study has been conducted focusing on SNS users and analysing their behaviours dynamically. In this paper, we used probability models such as Pareto/NBD and BG/NBD to predict customer lifetime vitality. The data we used include information on users’ tweet and retweet behaviour, such as recency and frequency. Our results showed that both Pareto/NBD model and BG/NBD model showed effective ability to fit and predict SNS users’ usage behaviour on microblog website. Tweet behaviors are more suitable for such probability models than retweet behaviors. Managerial implications of the two models should be highlighted as well. Interaction rate and dropout rate can be considered as the vitality index of the whole user base measuring how active users are and how likely a user is active. Managerial questions such as how active the users are in this platform now and how active the users will be in the future can be answered by applying those models.