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Marketing campaigns are a set of strategic activities that can promote abusiness's goal. The effect prediction for marketing campaigns in a realindustrial scenario is very complex and challenging due to the fact that priorknowledge is often learned from observation data, without any intervention forthe marketing campaign. Furthermore, each subject is always under theinterference of several marketing campaigns simultaneously. Therefore, wecannot easily parse and evaluate the effect of a single marketing campaign. Tothe best of our knowledge, there are currently no effective methodologies tosolve such a problem, i.e., modeling an individual-level prediction task basedon a hierarchical structure with multiple intertwined events. In this paper, weprovide an in-depth analysis of the underlying parse tree-like structureinvolved in the effect prediction task and we further establish a HierarchicalCapsule Prediction Network (HapNet) for predicting the effects of marketingcampaigns. Extensive results based on both the synthetic data and real datademonstrate the superiority of our model over the state-of-the-art methods andshow remarkable practicability in real industrial applications.

Hierarchical Capsule Prediction Network for Marketing Campaigns Effect