Attributed, But Not Incremental: Cannibalization-Corrected Attribution for Large-Scale Advertising
arXiv:2606.26690v1 Announce Type: cross 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 correc
The increasing sophistication and scale of digital advertising and attribution models, paired with the drive for more efficient spending, necessitates advanced methods to accurately measure incremental value.
Accurate attribution is crucial for optimizing multi-billion dollar advertising budgets, potentially leading to significant improvements in ROI and competitive advantage for companies relying on paid acquisition.
This research introduces a method to correct for cannibalization in advertising attribution, shifting from potentially overstated 'attributed conversions' to truer 'incremental growth', which refines budget allocation and channel diagnosis.
- · Digital advertising platforms
- · Companies with large advertising budgets
- · Data scientists and marketing analysts
- · Digital marketing agencies
- · Inefficient advertising channels
- · Legacy attribution models
Companies will reallocate advertising budgets based on more accurate incremental ROI measurements.
Increased competition among advertising channels as performance is more critically evaluated based on incremental contribution.
A shift towards more sophisticated measurement and experimentation capabilities becoming a core competency for growth-focused organizations.
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Read at arXiv cs.LG