An empirical study on the effect of user engagement on personalized free-content promotion based on a causal machine learning model

Abstract

Many digital platforms have employed free-content promotion strategies to deal with the high uncertainty levels regarding digital content products. However, the diversity of digital content products and user heterogeneity in content preference may blur the impact of platform promotions across users and products. Therefore, free-content promotion strategies should be adapted to allocate marketing resources optimally and increase revenue. This study develops personalized free-content promotion strategies based on individual-level heterogeneous treatment effects and explores the causes of their heterogeneity, focusing on the moderating effect of user engagement-related variables. To this end, we utilize random field experimental data provided by a top Chinese e-book platform. We employ a framework that combines machine learning with econometric causal inference methods to estimate individual treatment effects and analyze their potential mechanisms. The analysis shows that, on average, free-content promotions lead to a significant increase in consumer payments. However, the higher the level of user engagement, the lower the payment lift caused by promotions, as more-engaged users are more strongly affected by the cannibalization effect of free-content promotion. This study introduces a novel causal research design to help platforms improve their marketing strategies.