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IAB measurement survey: MMM at 61%, ghost bids at 46%, MTA declining to 40%
Luca Fiaschi has published an analysis of a recent IAB measurement survey: MMM at 61%, ghost bids at 46%, multi-touch attribution at 40%, geo-tests at 26%, with only 4% using nothing. The numbers exceed 100% because brands are stacking methods by necessity.
What changed
Fiaschi notes MTA's decline is structural: post-ATT, what vendors sell as MTA is largely modelled paths with only 20–35% of touches actually observed. MMM's rise is driven partly by open-source libraries such as PyMC-Marketing, but 75% of advertisers in the same survey report that measurement still underperforms: reflecting that MMM is easy to build but hard to calibrate correctly without real experiments that feed informative priors.
Why it matters for advertisers
For you, this means treating MMM outputs as hypotheses requiring calibration rather than conclusions: without incrementality tests, wide credible intervals make budget recommendations from MMM models unreliable enough to support opposite decisions simultaneously.
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IAB measurement survey: MMM at 61%, ghost bids at 46%, MTA declining to 40%
Luca Fiaschi has published an analysis of a recent IAB measurement survey: MMM at 61%, ghost bids at 46%, multi-touch attribution at 40%, geo-tests at 26%, with only 4% using nothing. The numbers exceed 100% because brands are stacking methods by necessity. Fiaschi notes MTA's decline is structural: post-ATT, what vendors sell as MTA is largely modelled paths with only 20–35% of touches actually observed. MMM's rise is driven partly by open-source libraries such as PyMC-Marketing, but 75% of advertisers in the same survey report that measurement still underperforms: reflecting that MMM is easy to build but hard to calibrate correctly without real experiments that feed informative priors.
For you, this means treating MMM outputs as hypotheses requiring calibration rather than conclusions: without incrementality tests, wide credible intervals make budget recommendations from MMM models unreliable enough to support opposite decisions simultaneously.
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