Temporal Uplift Modeling for Online Marketing

摘要

In recent years, uplift modeling, also known as individual treatment effect (ITE) estimation, has seen wide applications in online marketing, such as delivering one-time issuance of coupons or discounts to motivate users' purchases. However, complex yet more realistic scenarios involving multiple interventions over time on users are still rarely explored. The challenges include handling the bias from time-varying confounders, determining optimal treatment timing, and selecting among numerous treatments. In this paper, to tackle the aforementioned challenges, we present a temporal point process-based uplift model (TPPUM) that utilizes users' temporal event sequences to estimate treatment effects via counterfactual analysis and temporal point processes. In this model, marketing actions are considered as treatments, user purchases as outcome events, and how treatments alter the future conditional intensity function of generating outcome events as the uplift. Empirical evaluations demonstrate that our method outperforms existing baselines on both real-world and synthetic datasets. In the online experiment conducted in a discounted bundle recommendation scenario involving an average of 3 to 4 interventions per day and hundreds of treatment candidates, we demonstrate how our model outperforms current state-of-the-art methods in selecting the appropriate treatment and timing of treatment, resulting in a 3.6% increase in application-level revenue.