Attributed, But Not Incremental: Cannibalization-Corrected Attribution for Large-Scale Advertising

2026-06-25

AI Summary

Paid media attribution often overestimates true incremental lift in the presence of channel overlap, leading to inaccurate ROI assessment and suboptimal budget allocation. This work proposes the first calibration framework that integrates incremental experiments with large-scale attribution systems: leveraging causal insights from incrementality trials as anchors, it translates sparse lift observations into daily corrected estimates and allocates cross-channel cannibalization effects under business-level hierarchical constraints. Combining causal inference, hierarchical optimization, and machine learning–based attribution models, the approach substantially reduces calibration error in offline evaluations. Following global deployment across multiple markets, it has driven strategic budget reallocations and achieved an empirical reduction in measured cannibalization rates by approximately 15 percentage points, effectively bridging the gap between attribution and true incrementality.

Abstract

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.

Problem

• attribution
• cannibalization
• incrementality
• advertising
• budget allocation

Innovation

• attribution correction
• incrementality experiments
• cannibalization
• causal calibration
• budget allocation