Attribution
Marketing Mix Modeling
A statistical approach that uses regression analysis on historical data to estimate the contribution of each marketing channel to business outcomes. Works at an aggregate level (not user-level) making it privacy-safe and resilient to tracking changes. Takes into account external factors like seasonality, promotions, and economic conditions. Typically requires multiple years of historical data and works best for high-spend brands across multiple channels. Slower to implement than MTA but provides a more holistic and unbiased view of channel effectiveness.
Why it matters
It is the measurement approach least affected by tracking loss, because it works on aggregate spend and revenue rather than user-level journeys.
In practice
It needs two to three years of weekly data with genuine spend variation. A channel whose budget never moved cannot have its effect estimated.
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