![]() This approach is appropriate if the data available is only aggregated at the national level (e.g. The LightweightMMM can either be run using data aggregated at the national level (standard approach) or using data aggregated at a geo level (sub-national hierarchical approach). Where kpi is typically the volume or value of sales per time period, $\alpha$ is the model intercept, $trend$ is a flexible non-linear function that captures trends in the data, $seasonality$ is a sinusoidal function with configurable parameters that flexibly captures seasonal trends, $media\ channels$ is a matrix of different media channel activity (typically impressions or costs per time period) which receives transformations depending on the model used (see Media Saturation and Lagging section) and $other\ factors$ is a matrix of other factors that could influence sales. $$kpi = \alpha trend seasonality media\ channels other\ factors$$ the KPI could be sales per week), however, it can also be run at the daily level. An MMM is typically run using weekly level observations (e.g. A simplified model overview is shown below and the full model is set out in the model documentation. Theory Simplified Model OverviewĪn MMM quantifies the relationship between media channel activity and sales, while controlling for other factors. The LightweightMMM package (built using Numpyro and JAX) helps advertisers easily build Bayesian MMM models by providing the functionality to appropriately scale data, evaluate models, optimise budget allocations and plot common graphs used in the field. Construct hierarchical models, with generally tighter credible intervals, using breakout dimensions such as geography.Report on both parameter and model uncertainty and propagate it to your budget optimisation.Utilise information from industry experience or previous media mix models using Bayesian priors.Taking a Bayesian approach to MMM allows an advertiser to integrate prior information into modelling, allowing you to: Investigate effects on your target KPI (such as sales) by media channel. ![]()
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