Refresh Frequency
Model refreshes can be done as frequently as you would like. Model selection and tuning is typically an iterative process, which may need to be refreshed along with new data. You might consider updating the model quarterly, annually, or at a frequency that matches your marketing budget decision making process.
Recommended: incorporate new data by adding it to the older data
We recommend adding the new data to the older data and running Meridian. One ought to consider whether or not to discard the oldest data to accommodate the new data. This may be necessary to stay in the 2-3 year data window that's common in an MMM. Meridian doesn't model media effectiveness as time-varying. So, the decision to discard old data when appending new data is a bias-variance trade-off. A longer time window reduces variance because you have more data, but it can increase bias if media effectiveness and strategies have changed drastically over time.
Recognize that MMM estimates often exhibit high variance. This can mean that incorporating even a relatively small amount of new data may have a noticeable effect on the model's results. For this reason, there can be valid business reasons to set the priors in the new model to encourage the posterior of the new model to match the posterior of the old model. We recommend that you set priors based on prior knowledge and intuition, and it is reasonable for this intuition to be informed by past MMM results. It is your decision as to how strongly you want past MMM results to inform your prior knowledge and intuition. However, consider that setting priors that match an old MMM's results effectively counts the old data twice.
Alternative: model new data disjointly and use priors
Some may consider incorporating new data by fitting a model to just that new data, disjointly from the data used in old models. Although technically possible, even for a small amount of data such as a quarter, this is generally not recommended.
Modeling the new data completely disjointly from the old data won't properly consider lagged effects. Meridian allows media data to include more (older) time periods than the KPI and controls data. This allows the lagged effects to be more accurately modeled beginning with the first time period of KPI data. It is best to include max_lag time periods of media data prior to the first time period of KPI data whenever possible.
A small amount of new data is likely not informative enough for the model to make conclusions (see Amount of data needed). One may want to incorporate the information from the old data by using a prior distribution informed by the posterior of the older model. While the full joint posterior distribution of all parameters theoretically contains all information from older data (and using it as a prior for new data would be similar to fitting a new model that combines both old and new data), Meridian uses independent prior distributions for individual parameters. Therefore, even if the posterior distribution for each individual parameter were carried over as its prior, it might not fully capture the complete joint posterior distribution, which accounts for interdependencies between parameters. Additionally, Bayesian models require a parametric prior distribution for each parameter. MCMC sampling provides an empirical sample from the posterior, which may or may not have a suitable parametric approximation for direct use as a prior.