Multiple linear regression is a statistical technique frequently employed in Marketing Mix Modeling (MMM). It’s used to predict a dependent variable (the marketing output, such as sales or conversions) based on the influence of two or more independent variables (the marketing inputs, such as spend on different channels). Within this model, variables can be mathematically transformed to accurately account for complex real-world effects, such as delayed effects (e.g., ad exposure taking time to translate to sales) and the impact of diminishing returns (where additional spend yields progressively smaller returns).
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