A new research paper from arXiv proposes a framework to correct attribution errors in large-scale advertising systems, particularly for platforms like TikTok. The proposed method uses incrementality experiments to calibrate attribution outputs, addressing the issue where paid conversions might overstate true growth due to overlapping channels. When deployed on TikTok, this system helped reduce the measured cannibalization rate by approximately 15 percentage points, leading to more accurate budget allocation and strategy adjustments. AI
IMPACT Improves ROI measurement and budget allocation in large-scale advertising by correcting attribution errors.
RANK_REASON The cluster contains a research paper published on arXiv.
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