Online e-commerce applications are becoming a primary vehicle for people to find, compare, and ultimately purchase products. One of the fundamental questions that arises in e-commerce is to characterize, understand, and model user long-term purchasing intent, which is important as it allows for personalized and context relevant e-commerce services.

In this paper we study user activity and purchasing behavior with the goal of building models of time-varying user purchasing intent. We analyze the purchasing behavior of nearly three million Pinterest users to determine short-term and long-term signals in user behavior that indicate higher purchase intent. We find that users with long-term purchasing intent tend to save and clickthrough on more content. However, as users approach the time of purchase their activity becomes more topically focused and actions shift from saves to searches. We further find that purchase signals in online behavior can exist weeks before a purchase is made and can also be traced across different purchase categories. Finally, we synthesize these insights in predictive models of user purchasing intent. Taken together, our work identifies a set of general principles and signals that can be used to model user purchasing intent across many content discovery applications.

Filed under: Classification