marketingmixmodellen

Marketingmixmodellen, or the marketing mix model, is a statistical approach used to quantify how marketing activities influence sales and brand performance. In Scandinavian usage the term refers to models that connect inputs such as advertising, promotions, price, and distribution to outcomes over time, with the aim of improving budget allocation and forecasting.

Typically based on time-series data, MMM combines variables for media exposure and spend across channels (television, radio, print, digital), price promotions, trade and distribution, seasonality, and macro factors. The models are often multiplicative or log-linear, include lag terms to reflect delayed effects, and account for interactions or diminishing returns between channels.

Outputs include estimated elasticities, incremental lift, and return on investment. The results support scenario planning, optimization of media mix, and forecasting future sales under different spend levels. A standard MMM workflow covers data gathering, model specification, estimation, validation on holdout data, and periodic updating.

Limitations include data quality requirements, potential confounding and multicollinearity, model misspecification, and the challenge of capturing long-term brand effects. Results are sensitive to historical patterns and may not fully generalize to new markets or changing competitive dynamics.

Historically the concept derives from mid-20th century marketing theory of the marketing mix, with the 4Ps framework popularized in the 1960s. Modern MMM often blends econometric techniques with Bayesian methods or machine learning and may be integrated with digital attribution and marketing mix optimization to support strategic decisions.