Multi-touch attribution vs. media mix modeling: complements, not rivals

Multi-touch attribution vs. media mix modeling: complements, not rivals

Which answers "where did this conversion come from" — and which answers "what should next quarter's budget be"? Practitioners conflate them constantly.

The question of resolution
Multi-touch attribution (MTA) works at the user level: it stitches individual touchpoints into journeys using cookies, device IDs, or logged-in identity. Media mix modeling (MMM) works at the aggregate level: it regresses weekly or monthly conversions against weekly spend per channel, plus controls for seasonality, price, and promotions.

What each can and cannot see
MTA is blind to anything it cannot track — connected TV, podcast, out-of-home, and now most of Safari and iOS post-ATT (App Tracking Transparency). MMM sees those channels because it only needs aggregate spend, but it cannot tell you which creative or audience worked, and it needs 2-3 years of history with real spend variation to fit reliable coefficients.

The methodological catch
MMM is a regression on observational data, so multicollinearity is the silent killer: if you always raise search and social budgets together, the model cannot separate their effects. MTA, meanwhile, inherits all of last-click's correlation-not-causation problems unless paired with experiments.

The emerging consensus
Leading measurement teams run a "triangulation" stack — MMM for strategic budget allocation, MTA for tactical in-channel optimization, and incrementality experiments to calibrate both.

Bottom line for practitioners: Stop asking which is correct. MMM sets the macro budget, MTA optimizes within it, and lift tests keep both honest. If you can only afford one, your channel mix decides: heavy on untrackable media means MMM.