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Refresh the model
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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.
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Last updated 2025-06-11 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-06-11 UTC."],[[["\u003cp\u003eModel refresh frequency should align with data frequency and marketing team's decision-making timeframe (e.g., quarterly).\u003c/p\u003e\n"],["\u003cp\u003eExpanding the data window with each refresh allows older data to influence newer estimates while balancing bias and variance.\u003c/p\u003e\n"],["\u003cp\u003eEven small data additions can significantly impact results due to the inherent high-variance nature of MMM estimates.\u003c/p\u003e\n"],["\u003cp\u003ePrior settings can be adjusted to balance new and old data influences, informed by past results and business intuition.\u003c/p\u003e\n"]]],["Model refreshing frequency should align with data frequency and marketing decision timelines, such as quarterly updates for quarterly decisions. Appending new data reduces variance but may introduce bias if media strategies change. Appending small amounts of data can significantly impact estimates due to their high variance. When appending, setting priors to match previous results can align old and new data, although this risks double-counting data. Prior knowledge should influence prior selection, and prior MMM results can inform this.\n"],null,["# Refresh the model\n\nRefresh Frequency\n-----------------\n\nModel refreshes can be done as frequently as you would like. Model selection and\ntuning is typically an iterative process, which may need to be refreshed along\nwith new data. You might consider updating the model quarterly, annually, or at\na frequency that matches your marketing budget decision making process.\n\nRecommended: incorporate new data by adding it to the older data\n----------------------------------------------------------------\n\nWe recommend adding the new data to the older data and running Meridian.\nOne ought to consider whether or not to discard the oldest data to accommodate\nthe new data. This may be necessary to stay in the 2-3 year data window that's\ncommon in an MMM. Meridian doesn't model media effectiveness as\ntime-varying. So, the decision to discard old data when appending new data is a\nbias-variance trade-off. A longer time window reduces variance because you have\nmore data, but it can increase bias if media effectiveness and strategies have\nchanged drastically over time.\n\nRecognize that MMM estimates often exhibit high variance. This can mean that\nincorporating even a relatively small amount of new data may have a noticeable\neffect on the model's results. For this reason, there can be valid business\nreasons to set the priors in the new model to encourage the posterior of the new\nmodel to match the posterior of the old model. We recommend that you set priors\nbased on prior knowledge and intuition, and it is reasonable for this intuition\nto be informed by past MMM results. It is your decision as to how strongly you\nwant past MMM results to inform your prior knowledge and intuition. However,\nconsider that setting priors that match an old MMM's results effectively counts\nthe old data twice.\n\nAlternative: model new data disjointly and use priors\n-----------------------------------------------------\n\nSome may consider incorporating new data by fitting a model to just that new\ndata, disjointly from the data used in old models. Although technically\npossible, even for a small amount of data such as a quarter, this is generally\nnot recommended.\n\nModeling the new data completely disjointly from the old data won't properly\nconsider lagged effects. Meridian allows media data to include more (older) time\nperiods than the KPI and controls data. This allows the lagged effects to be\nmore accurately modeled beginning with the first time period of KPI data. It is\nbest to include max_lag time periods of media data prior to the first time\nperiod of KPI data whenever possible.\n\nA small amount of new data is likely not informative enough for the model to\nmake conclusions (see Amount of data needed). One may want to incorporate the\ninformation from the old data by using a prior distribution informed by the\nposterior of the older model. While the full joint posterior distribution of all\nparameters theoretically contains all information from older data (and using it\nas a prior for new data would be similar to fitting a new model that combines\nboth old and new data), Meridian uses independent prior distributions for\nindividual parameters. Therefore, even if the posterior distribution for each\nindividual parameter were carried over as its prior, it might not fully capture\nthe complete joint posterior distribution, which accounts for interdependencies\nbetween parameters. Additionally, Bayesian models require a parametric prior\ndistribution for each parameter. MCMC sampling provides an empirical sample from\nthe posterior, which may or may not have a suitable parametric approximation for\ndirect use as a prior."]]