Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy
This provides a reusable playbook for marketers to operationalize causal targeting at scale, addressing the problem of aligning campaigns with strategic KPIs while setting guardrails.
The paper tackles the problem of optimizing marketing targeting strategies by converting heterogeneous-treatment uplift into constrained allocation decisions to maximize revenue and retention while adhering to business guardrails. The result is a framework that outperforms baselines in offline evaluations and shows strategic lift in online A/B tests, validated with metrics like uplift AUC and revenue gains.
This paper introduces a marketing decision framework that converts heterogeneous-treatment uplift into constrained targeting strategies to maximize revenue and retention while honoring business guardrails. The approach estimates Conditional Average Treatment Effects (CATE) with uplift learners and then solves a constrained allocation to decide who to target and which offer to deploy under limits such as budget or acceptable sales deterioration. Applied to retention messaging, event rewards, and spend-threshold assignment, the framework consistently outperforms propensity and static baselines in offline evaluations using uplift AUC, Inverse Propensity Scoring (IPS), and Self-Normalized IPS (SNIPS). A production-scale online A/B test further validates strategic lift on revenue and completion while preserving customer-experience constraints. The result is a reusable playbook for marketers to operationalize causal targeting at scale, set guardrails, and align campaigns with strategic KPIs.