This paper presents a model for identifying general goals of anonymous consumers visiting a retail website. When visiting a transactional website, consumers have various goals such as browsing or purchasing a particular product during their current visit. By predicting these goals early in the visit, online merchants could personalize their offer to better fulfill the needs of consumers. Most visitors remain anonymous to the website, however personalization systems require demographic and transaction history data which is available only for registered and logged-on users. We propose a simple model which enables classifying anonymous visitors according to their goals after only a few traversals (clicks). The model is based solely on navigational patterns which can be automatically extracted from clickstream. Theoretical and managerial implications are presented.