Social tagging is a new way to share and categorize content, allowing users to express their thoughts, perceptions, and feelings with respect to concepts such as products or brands with their own keywords, “tags.” With this system, products can be connected through user-generated keywords, and the rich associative information in social tagging networks provides marketers with opportunities to infer customers’ mental schema toward a product. This research investigates whether the semantic position of products on social tagging networks can predict sales dynamics. This research shows that product positioning maps utilizing products-to-tags networks have distinct informational value in predicting sales dynamics. More specifically, this research suggests that (1) books in long tail can increase sales by being strongly linked to socially popular keywords and well-known keywords with high degree centrality and (2) top sellers can be better sellers by creating dense content clusters rather than connecting them to well-known keywords with high degree centrality. Our findings suggest that marketing managers understand better a user community’s perception of products and potentially influence product sales by taking into account the positioning of their products within social tagging networks.