Meridian is the official evolution of the Google MMM approach. It is the updated version of LightweightMMM. Both versions are based on Google's Bayesian MMM research since 2017.
The key features of Meridian are reach and frequency modeling, handling paid search effectively, and experiment calibration.
How to migrate to Meridian
To migrate from LightweightMMM to Meridian, you install Meridian and import your data using the same process as any new user to Meridian. For more information, see Install Meridian.
Feature comparison
The input data for both models is the same.
The following chart gives an overview of the key feature differences between the projects:
| Feature | LightweightMMM | Meridian |
|---|---|---|
| Language | Python | Python |
| Bayesian library | Numpyro | Tensorflow Probability |
| Experiment calibration | Possible but manual | Yes |
| Reach and frequency modeling | No | Yes |
| Optimizer | Yes | Yes |
| ROI formulation of the model | No | Yes |
| Incorporating GQV confounder | Possible but manual | Yes |
| National- and geo-level models | Yes | Yes, national plus more geos |
| Trend and seasonality | Straight line + sinusoidal repeating shape (daily, weekly) | Knots |
| Custom priors | Yes | Yes |
| Lagging and saturation transformation | Yes | Yes |
| Scaling of inputs | Manual | Automatic |
Differences in the model specifications
LightweightMMM offers three different model architectures: Adstock, Hill-Adstock, and Carryover. Meridian uses a variation of the Hill-Adstock architecture, and does not allow other architectures. You can choose the order in which the Hill- and Adstock-transformations are applied for the Meridian baseline model. The Meridian reach and frequency model has a fixed Hill-Adstock order: Hill first, and then Adstock.
Other differences between Meridian and LightweightMMM include:
Media channels are hierarchical across geos in both projects. However, in LightweightMMM, the geo hierarchy doesn't add additional free parameters. Instead, one media coefficient is used to specify both the hyper-prior and the individual geo-level media channel priors in LightweightMMM. Meridian has an additional parameter
eta_mthat specifies the standard deviation of the media coefficient across geos. Meridian also allows the hierarchical variation to be either normal or log-normal in shape.The non-media features, called control variables in Meridian, are also hierarchical in Meridian, whereas they are non-hierarchical across geos in LightweightMMM. The Meridian model parameter
xi_cspecifies the standard deviation of this geo hierarchy.Meridian lets you specify media priors either in terms of beta (the same as LightweightMMM) or in terms of ROI.
The baseline is expressed differently in Meridian, compared to LightweightMMM. With Meridian, users can specify both geo-level and time-level fixed effects, and the baseline is the sum of both fixed effects.
Expected differences in the MCMC sampling time
Due to more model parameters and model complexity in Meridian, MCMC sampling in Meridian is expected to take longer than in LightweightMMM. However, because the models are relatively similar, Meridian is not expected to take much longer than LightweightMMM. Precise estimates on how much longer depends on the compute environment, number of geos, model tuning parameters, priors, data, and other factors. Although Meridian's model complexity likely leads to longer MCMC sampling time, more accurate results are expected.