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FunnelCausalNet is an uplift estimator designed to optimize coupon allocation by integrating a binary conversion head with a nonnegative conditional-value head, leveraging the relationship between gross merchandise value (GMV) and conversion rates. The model demonstrates a significant reduction in GMV effect error compared to traditional GMV regression methods, achieving an 18-48% improvement across various zero-inflation scenarios. In extensive evaluations on both semi-synthetic and real-world datasets, FunnelCausalNet consistently outperforms eleven baselines, achieving superior DeltaROI across multiple tiers of coupon allocation.
FunnelCausalNet reduces GMV effect error by up to 48%, redefining how coupon campaigns can maximize both conversion and revenue.
Coupon campaigns seek to lift both conversion and revenue, but gross merchandise value (GMV) follows a deterministic funnel from conversion to conditional order value and is zero-inflated and heavy-tailed. We propose FunnelCausalNet, an uplift estimator coupling a binary conversion head with a nonnegative conditional-value head through $\mu_{\mathrm{gmv}}=\mu_{\mathrm{conv}}\mu_{\mathrm{val}}$. Under explicit RCT, support, rate-gap, and cross-head covariance-control assumptions, an idealized leading-order MSE comparison identifies a regime in which funnel composition can reduce pointwise variance; this is a heuristic, not a guarantee for the shared-representation neural model. The estimator is paired with marginal split-conformal CATE summaries, combined through a Bonferroni union as audit bands, and a Lagrangian budgeted allocator using RCT-anchored estimates for subsidy-aware ROI accounting. On semi-synthetic multi-tier Criteo-MT7, FunnelCausalNet's mean AUUC_GMV is within one seed standard deviation of the leading feature-interaction baseline among eleven baselines, while a controlled ablation reduces GMV effect error versus direct GMV regression by 18--48% across tested zero-inflation regimes. On de-identified industrial Hotel-Coupon RCT logs with about 4.9 million hold-out exposure records per seed, expected-outcome evaluation sweeps full LP frontiers; FunnelCausalNet has the best seed-averaged mean DeltaROI at all seven correlated anchors from 10% to 60%, which we treat as descriptive frontier consistency rather than independent significance. On sparse binary-spend public benchmarks, revenue-focused rankers can dominate uplift-curve proxies, defining an explicit regime boundary.
