Hierarchical Capsule Prediction Network for Marketing Campaigns Effect

TL;DR

This paper introduces HapNet, a hierarchical capsule network designed to predict individual marketing campaign effects amidst complex, intertwined influences, outperforming existing methods in synthetic and real data scenarios.

Contribution

The paper proposes a novel hierarchical capsule network model specifically for modeling and predicting marketing campaign effects at the individual level.

Findings

HapNet outperforms state-of-the-art methods in experiments.

The model demonstrates high practicability in real industrial applications.

Effective in modeling complex, intertwined marketing effects.

Abstract

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.