Interpretable Multiple Treatment Revenue Uplift Modeling

Big data and business analytics are critical drivers of business and societal transformations. Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment. Prior work examines models for single treatments and binary customer responses. The paper extends corresponding approaches by developing uplift models for multiple treatments and continuous outcomes. This facilitates selecting an optimal treatment from a set of alternatives and estimating treatment effects in the form of business outcomes of continuous scale. Another contribution emerges from an evaluation of an uplift model's interpretability, whereas prior studies focus almost exclusively on predictive performance. To achieve these goals, the paper develops revenue uplift models for multiple treatments based on a recently introduced algorithm for causal machine learning, the causal forest. Empirical experimentation using two real-world marketing data sets demonstrates the advantages of the proposed modeling approach over benchmarks and standard marketing practices.

Response Transformation and Profit Decomposition for Revenue Uplift Modeling

Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize incremental sales. The paper introduces uplift modeling strategies for maximizing incremental revenues. If customers differ in their spending behavior, revenue maximization is a more plausible business objective compared to maximizing conversions. The proposed methodology entails a transformation of the prediction target, customer-level revenues, that facilitates implementing a causal uplift model using standard machine learning algorithms. The distribution of campaign revenues is typically zero-inflated because of many non-buyers. Remedies to this modeling challenge are incorporated in the proposed revenue uplift strategies in the form of two-stage models. Empirical experiments using real-world e-commerce data confirm the merits of the proposed revenue uplift strategy over relevant alternatives including uplift models for conver-sion and recently developed causal machine learning algorithms. To quantify the degree to which improved targeting decisions raise return on marketing, the paper develops a decomposition of campaign profit. Applying the decomposition to a digital coupon targeting campaign, the paper provides evidence that revenue uplift modeling, as well as causal machine learning, can improve cam-paign profit substantially.

Affordable Uplift: Supervised Randomization in Controlled Experiments

Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment assignment is costly, since random targeting deviates from an established targeting policy. To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. It is a novel approach that integrates existing scoring models into randomized trials to target relevant customers, while ensuring consistent estimates of treatment effects through correction for active sample selection. An empirical Monte Carlo study shows that data collection under supervised randomization is cost-efficient, while downstream uplift models perform competitively.