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Marketing mix modeling (MMM) (sometimes also called Media Mix Modeling) collects aggregated data across marketing and non-marketing factors over a multi-year historical period. That data is used to develop a demand model which quantifies the historical contribution of each marketing and non-marketing input to business outcome. MMM typically estimates marketing impact on historical business outcomes at the channel level probabilistically, and can be subject to the correlation vs. causation dilemma. For forward looking projections MMM relies on a number of assumptions for non-marketing factors as well the assumption that channel level media mix, cost, and response does not diverge with the historic data that is the basis for the demand model. While well built models based on high-quality data can overcome the correlation vs. causation dilemma to provide channel lift and forecasts, the limitation on degrees of freedom and challenges with overspecified models means that they cannot be used to inform tactical decision making at the sub-channel level. Because models rely on multiple years of historical data to determine an average read for marketing inputs, they are challenged in sussing out dynamic changes to marketing channels and/or business changes in recent periods.