Modeling behavioral dynamics in digital content consumption : an attention-based neural point process approach with applications in video games

Abstract

The consumption of digital content products (e.g., video games and live streaming) is often associated with multifaceted, dynamically interacting consumer behavior that is subject to influence from pertinent external events. Inspired by these characteristics, we develop a novel attention-based neural point process approach to holistically capture the richness and complexity of consumer behavioral dynamics in modern digital content consumption. Our model features a new multirepresentational, continuous-time attention mechanism that can flexibly model dynamic interactions between different types of behavior under external influence. Using learned representations as sufficient statistics of past events, we build a marked point process to efficiently characterize the occurrence time, behavior combination, and consumption quantity of consumers’ future activities. We illustrate our model development and applications in the empirical context of a sports video game, showing its superior predictive performance over a wide range of baseline methods. Leveraging individual-level parameter estimates, we further demonstrate our model’s utility for conducting segmentation analysis and evaluating the effects of past events on consumers’ future engagement. Our model provides managers and practitioners with a powerful tool for developing more effective and targeted marketing strategies and gaining insights into consumer behavioral dynamics in digital content consumption.