Hierarchical Capsule Prediction Network for Marketing Campaigns Effect
This addresses a complex, real-world challenge in marketing analytics for businesses, but appears incremental as it builds on existing capsule network concepts for a specific domain.
The paper tackles the problem of predicting individual-level effects of marketing campaigns when multiple campaigns interfere simultaneously, by proposing a Hierarchical Capsule Prediction Network (HapNet) that models a hierarchical structure, and demonstrates superiority over state-of-the-art methods on synthetic and real data.
Marketing campaigns are a set of strategic activities that can promote a business's goal. The effect prediction for marketing campaigns in a real industrial scenario is very complex and challenging due to the fact that prior knowledge is often learned from observation data, without any intervention for the marketing campaign. Furthermore, each subject is always under the interference of several marketing campaigns simultaneously. Therefore, we cannot easily parse and evaluate the effect of a single marketing campaign. To the best of our knowledge, there are currently no effective methodologies to solve such a problem, i.e., modeling an individual-level prediction task based on a hierarchical structure with multiple intertwined events. In this paper, we provide an in-depth analysis of the underlying parse tree-like structure involved in the effect prediction task and we further establish a Hierarchical Capsule Prediction Network (HapNet) for predicting the effects of marketing campaigns. Extensive results based on both the synthetic data and real data demonstrate the superiority of our model over the state-of-the-art methods and show remarkable practicability in real industrial applications.