Modeling Behavioral Dynamics in Digital Content Consumption: An Attention-Based Neural Point Process Approach with Applications in Video Games

• Published in: Marketing science (Providence, R.I.)
• Main Authors: Yin, Junming, Feng, Yue (Katherine), Liu, Yong
• Format: Journal Article
• Language: English
• Published: 18-07-2024
• ISSN: 0732-2399; 1526-548X
• DOI: 10.1287/mksc.2020.0180

This paper develops a novel attention-based neural point process approach to model behavioral dynamics and predict future activities of consumers in digital content consumption.
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.
History: Yuxin Chen served as the senior editor.
Funding: J. Yin was partly supported by the Adobe Digital Experience Research Award and the Amazon AWS Machine Learning Research Award. Y. (K.) Feng was supported by the Research Grants Council of Hong Kong [ECS Grant 25508819]. Y. Liu gratefully acknowledges the support from Bob Eckert, chairman of the board at Levi Struss and former CEO and chairman at Mattel, through the Robert A. Eckert endowed chair in marketing at the University of Arizona.
Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mksc.2020.0180 .