MulUBA: multi-level visual analytics of user behaviors for improving online shopping advertising

The advertising revenue of online shopping platforms comes from the users who click on the advertisements and purchases the advertised goods. Therefore, to accurately advertise and increase revenue, advertising analysts engage in discovering representative groups and their behavior patterns from the data of demographic attributes, shopping behaviors, and advertising click behaviors of a large number of users. Existing methods often represent user behaviors based on single-level user profiles. However, under different community granularity and time scales, user behaviors have different characteristics. In addition, the sequential relationship between advertisement clicks and other shopping behaviors is difficult to be accurately identified by the single-level analysis methods. Therefore, we cooperate with advertising experts and propose a multi-level visual analysis method based on the K-Means algorithm, which can better understand user behaviors from multiple community granularity and multiple time scales. We design two novel visualization diagrams and improve three traditional charts that can help analysts observe user characteristics at the three levels: user groups, user subgroups, and user individuals, as well as can analyze the time-series events such as advertising clicks and product purchases of representative users from multiple time scales. Furthermore, we implement a multi-view interactive prototype system MulUBA to help analysts put targeted advertisements and increase advertising revenue. Finally, we verify the effectiveness and usability of our approach by conducting three case studies and an expert evaluation on a real-world online shopping advertising dataset.