修正广告归因中的自我吞噬问题，提升预算分配准确性
Attributed, But Not Incremental: Cannibalization-Corrected Attribution for Large-Scale Advertising
- 用增量实验校准归因，将稀疏提升数据转化为每日修正值
- 在多个全球市场部署后，自我吞噬率降低约15个百分点
- 适合大规模广告系统优化与跨渠道预算决策者
在大规模付费获取与增长广告系统中，生产环境的归因输出广泛用于日常预算分配和渠道诊断。然而，当付费渠道与自然流量、品牌驱动流量或其他获客渠道重叠时，付费归因转化（如日新增用户）可能系统性夸大真实增量增长。这种归因-自我吞噬错配会扭曲增量投资回报率评估，影响规模化预算决策。本文提出一种基于实验校准的归因修正框架，利用增量实验作为因果锚点，将稀疏的提升测量值转化为每日修正估计。为使修正信号在生产粒度下可操作，我们进一步在结构一致性约束下将校准后的自我吞噬量分配至业务层级。离线前向时间验证显示，该框架相较于原始归因和细粒度机器学习基线，显著降低了校准误差。在多个全球TikTok市场部署后，系统支持了预算与流量策略调整，随后测量到的自我吞噬率下降约15个百分点。
原文摘要 · Abstract (English)
In large-scale paid acquisition and growth advertising systems, production attribution outputs are widely used for daily budget allocation and channel diagnosis. However, paid-attributed conversions such as daily new users (DNU) may systematically overstate true incremental growth when paid channels overlap with organic demand, brand-driven traffic, or other acquisition channels. This attribution-cannibalization mismatch can distort incremental ROI measurement and budget decisions at scale. We propose an experiment-calibrated attribution correction framework that uses incrementality experiments as causal anchors to convert sparse lift measurements into daily correction estimates. To make the corrected signal actionable at production granularity, we further allocate calibrated cannibalization volume across business hierarchies under structural consistency constraints. Offline forward-in-time validation against channel-level incrementality experiment readouts shows that the proposed framework substantially reduces calibration error relative to raw attribution and fine-grained ML baselines. Deployed across multiple global TikTok markets, the system supported budget and traffic strategy adjustments that were followed by an approximately 15-percentage-point reduction in the measured cannibalization rate.
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