<b>Can top-down and bottom-up attribution ever agree?</b>

<b>Can top-down and bottom-up attribution ever agree?</b>

The question: media mix modeling (MMM) — regression on aggregate spend and outcomes over time — and multi-touch attribution (MTA) — user-level path crediting — routinely produce conflicting channel ROIs. Practitioners treat this as a problem. It's actually expected.

Why they differ by design:
— <b>MMM</b> sees everything: offline, brand, competitive effects, seasonality, even channels with no click. But it's coarse, can't resolve individuals, and struggles to separate correlated channels (the multicollinearity problem).
— <b>MTA</b> is granular and per-user but blind to anything without a trackable touch — TV, podcast, word of mouth, and increasingly anything lost to privacy restrictions and cookie deprecation.

What the research says: the emerging consensus (and the basis of 'unified marketing measurement') is that MMM and MTA answer different questions at different altitudes. MMM bounds total channel contribution and captures un-tracked media; MTA allocates within the tracked, addressable portion. Calibration studies show MTA estimates anchored to MMM-derived totals — and ideally validated against incrementality experiments — are far more stable than either alone.

The nuance: agreement is not the goal. If MMM says paid social drives 12% of revenue and MTA says 18%, the gap itself is informative — it often reveals MTA's last-touch leakage or MMM's collinearity smearing two channels together.

What to actually do: build a measurement triangle. Let MMM set the ceiling, MTA distribute within it, and experiments settle disputes. Never let one model overrule the other silently.

<b>Bottom line for practitioners:</b> MMM and MTA aren't competitors to reconcile — they're two instruments calibrating each other, with experiments as the referee.