Customer Lifetime Value (LTV) prediction, a central problem in modern marketing, is characterized by a unique zero-inflated and long-tail data distribution. This distribution presents two fundamental challenges: (1) the vast majority of low-to-medium value users numerically overwhelm the small but critically important segment of high-value "whale" users, and (2) significant value heterogeneity exists even within the low-to-medium value user base. Common approaches either rely on rigid statistical assumptions or attempt to decouple ranking and regression using ordered buckets; however, they often enforce ordinality through loss-based constraints rather than inherent architectural design, failing to balance global accuracy with high-value precision. To address this gap, we propose \textbf{C}onditional \textbf{C}ascaded \textbf{O}rdinal-\textbf{R}esidual Networks \textbf{(CC-OR-Net)}, a novel unified framework that achieves a more robust decoupling through \textbf{structural decomposition}, where ranking is architecturally guaranteed. CC-OR-Net integrates three specialized components: a \textit{structural ordinal decomposition module} for robust ranking, an \textit{intra-bucket residual module} for fine-grained regression, and a \textit{targeted high-value augmentation module} for precision on top-tier users. Evaluated on real-world datasets with over 300M users, CC-OR-Net achieves a superior trade-off across all key business metrics, outperforming state-of-the-art methods in creating a holistic and commercially valuable LTV prediction solution.

翻译：客户终身价值（LTV）预测是现代营销中的一个核心问题，其数据分布具有独特的零膨胀和长尾特征。这种分布带来了两个基本挑战：（1）绝大多数中低价值用户在数量上淹没了数量虽少但至关重要的高价值“鲸鱼”用户群体；（2）即使在中低价值用户内部也存在显著的价值异质性。常见方法要么依赖于严格的统计假设，要么试图通过有序分桶来解耦排序和回归任务；然而，它们通常通过基于损失的约束而非固有的架构设计来强制排序性，未能平衡全局准确性与高价值用户预测精度。为弥补这一不足，我们提出了**条件级联序数-残差网络（CC-OR-Net）**，这是一个新颖的统一框架，通过**结构分解**实现更鲁棒的解耦，其中排序性在架构层面得到保证。CC-OR-Net集成了三个专用组件：用于鲁棒排序的*结构序数分解模块*、用于细粒度回归的*桶内残差模块*，以及用于提升顶级用户预测精度的*定向高价值增强模块*。在包含超过3亿用户的真实数据集上的评估表明，CC-OR-Net在所有关键业务指标上均实现了更优的权衡，在构建全面且具有商业价值的LTV预测解决方案方面超越了现有最先进方法。