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FAQs
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Is the library free?
Yes, the library is free to use and is open sourced on GitHub
for anyone to use.
Do we have to share our data with Google to use the library?
Google won't have access to your input data, model, or results (apart from
Google media data supplied through our MMM Data Platform). If you request data
from the Google MMM Data Platform, that is the only data that Google has
access to. But Google won't know whether you actually include that data in
your model. Your actual model inputs and outputs are entirely private, unless
you choose to share it with your Google representatives.
Migrating from LightweightMMM
As a current LightweightMMM user, is extra work needed to build data input
for Meridian?
To take full advantage of the new Meridian innovations, you will need
to add more data dimensions such as:
- Reach and frequency
- Experiments
- Google Query Volume (GQV)
You can still run Meridian without these dimensions, although you will
miss out on the new innovations. For more information, see Migrate from LightweightMMM
.
Data collection and cleaning
Can I collect all data types simultaneously (performance, YouTube reach and
frequency, Google Query Volume) in the MMM Data Platform
interface?
Performance data and YouTube reach and frequency data must be requested
separately. The request workflow is detailed in the User Guide that is
included with your MMM Data Platform access email.
What is the scope of the GQV data that I can request?
The Google Query Volume, the output includes:
- QueryLabel - Brand or generic
- ReportDate
- TimeGranularity (You can request Daily, Weekly_Sunday, or
Weekly_Monday data.)
- GeoCriteriaId
- GeoName
- GeoType
- IndexedQueryVolume - All query volume data is indexed. Raw numbers aren't
provided for Query Volume.
Modeling
For a given media lever, how can I set different priors associated with
different time periods?
The closest thing to this would be the roi_calibration_period
argument. Based on section 3.4 of the MMM calibration white paper
, we suggest calculating a spend-weighted average ROI for the experiments
and passing roi_calibration_period
to match the four quarters
of the experiments. If the experiments have very different standard errors,
you might want to further weight the experiments accordingly. For more
information, see Set the ROI calibration period
.
Can I put a temporal prior for the knot values?
Meridian does not support time varying priors for knot values.
Can I measure synergies between channels in Meridian?
Meridian doesn't support this kind of analysis.
Is it possible to get a temporal read-out of ROI with Meridian?
You can access the incremental outcome of each media channel over time, and
therefore calculate ROI:
- Take the estimated incremental outcome, as found
in
Analyzer().incremental_outcome()
.
- Use the
selected_times
option to choose the weeks of
interest.
- Divide by spend over those weeks. This gives you the ROI and reflects
the individual time period more accurately.
Important:
When tracking ROI over time, consider that even though the
coefficients in the model are not time-varying, the ROI can still change over
time because it is dependent on additional factors that might vary across
time. For example, the Hill curves model the non-linear, diminishing returns
of media execution, and therefore the amount of media execution at a given time
can impact the ROI. Addtionally, media allocation can vary across
geos over time with different effectiveness and the cost of media
execution can vary across time.
Interpretation and optimization
Can I measure the ROI of bidding strategies based on the bid targets set?
Google's MMM data feed provides bid strategy type (such as Maximize
Conversions and Target ROAS) by campaign, but the feed does not include the bid
target itself. To view this specific dimension, advertisers can source bid
strategy reports directly from Google Ads or work with their Google Account
Representative on a custom data solution.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License
, and code samples are licensed under the Apache 2.0 License
. For details, see the Google Developers Site Policies
. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-06-24 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 2025-06-24 UTC."],[[["\u003cp\u003eMeridian is an open-source MMM library currently in early access, best suited for advertisers with in-house MMM expertise and data.\u003c/p\u003e\n"],["\u003cp\u003eEarly access users are asked to provide feedback, participate in discussions on GitHub, and report issues.\u003c/p\u003e\n"],["\u003cp\u003eAccess is granted through an application form with limited spots, and invitations are sent quarterly.\u003c/p\u003e\n"],["\u003cp\u003eGoogle cannot see user data, model specifications, or results, unless data is requested from the Google MMM Data Platform.\u003c/p\u003e\n"],["\u003cp\u003eCurrent LightweightMMM users can migrate to Meridian but may need to add data dimensions to leverage new innovations.\u003c/p\u003e\n"]]],["The library is free and open-source on GitHub, with user data privacy maintained unless shared with Google representatives. Users can leverage new features by adding reach, frequency, experiment, and Google Query Volume (GQV) data. Performance and YouTube data are requested separately, while GQV includes indexed query data like Brand, ReportDate, and Geo details. Meridian does not support time-varying priors for knot values, measuring synergies between channels, or bid targets but enables calculating time-varying ROI by accessing incremental impact and dividing it by spend.\n"],null,["# FAQs\n\nGeneral product information\n---------------------------\n\n#### Is the library free?\n\n\nYes, the library is free to use and is open sourced on\n[GitHub](https://github.com/google/meridian) for anyone to use. \n\n#### Do we have to share our data with Google to use the library?\n\n\nGoogle won't have access to your input data, model, or results (apart from\nGoogle media data supplied through our MMM Data Platform). If you request data\nfrom the Google MMM Data Platform, that is the only data that Google has\naccess to. But Google won't know whether you actually include that data in\nyour model. Your actual model inputs and outputs are entirely private, unless\nyou choose to share it with your Google representatives.\n\nMigrating from LightweightMMM\n-----------------------------\n\n#### As a current LightweightMMM user, is extra work needed to build data input\nfor Meridian?\n\n\nTo take full advantage of the new Meridian innovations, you will need\nto add more data dimensions such as:\n\n- Reach and frequency\n- Experiments\n- Google Query Volume (GQV)\n\n\nYou can still run Meridian without these dimensions, although you will\nmiss out on the new innovations. For more information, see\n[Migrate from LightweightMMM](/meridian/docs/migrate).\n\nData collection and cleaning\n----------------------------\n\n#### Can I collect all data types simultaneously (performance, YouTube reach and\nfrequency, Google Query Volume) in the MMM Data Platform\ninterface?\n\n\nPerformance data and YouTube reach and frequency data must be requested\nseparately. The request workflow is detailed in the User Guide that is\nincluded with your MMM Data Platform access email. \n\n#### What is the scope of the GQV data that I can request?\n\n\nThe Google Query Volume, the output includes:\n\n- QueryLabel - Brand or generic\n- ReportDate\n- TimeGranularity (You can request Daily, Weekly_Sunday, or Weekly_Monday data.)\n- GeoCriteriaId\n- GeoName\n- GeoType\n- IndexedQueryVolume - All query volume data is indexed. Raw numbers aren't provided for Query Volume. \n\n#### Can I apply the GQV methodology for non-Google search data?\n\n\nOrganic query volume from non-Google search engines is often unavailable. Some\nalternative options are described in [Understanding query volume as a confounder for search ads](/meridian/docs/advanced-modeling/paid-search-modeling#understanding-query-volume-confounder).\n\nModeling\n--------\n\n#### For a given media lever, how can I set different priors associated with\ndifferent time periods?\n\n\nThe closest thing to this would be the `roi_calibration_period`\nargument. Based on section 3.4 of the [MMM calibration white paper](https://research.google/pubs/media-mix-model-calibration-with-bayesian-priors/), we suggest calculating a spend-weighted average ROI for the experiments\nand passing `roi_calibration_period` to match the four quarters\nof the experiments. If the experiments have very different standard errors,\nyou might want to further weight the experiments accordingly. For more\ninformation, see [Set the ROI calibration period](/meridian/docs/user-guide/configure-model#set-roi-calibration-period). \n\n#### Can I put a temporal prior for the knot values?\n\n\nMeridian does not support time varying priors for knot values. \n\n#### How can I get detailed decomposition information of the regression, such as\ngetting dataframes for the posterior draws?\n\n\nPosterior samples are in the `inference_data` object, and you can\nturn this array into any dataframe you need. To access the data samples using\nthe docstring, see [meridian.model.model.Meridian](/meridian/reference/api/meridian/model/model/Meridian). \n\n#### Can I measure synergies between channels in Meridian?\n\n\nMeridian doesn't support this kind of analysis. \n\n#### Is it possible to get a temporal read-out of ROI with Meridian?\n\n\nYou can access the incremental outcome of each media channel over time, and\ntherefore calculate ROI:\n\n1. Take the estimated incremental outcome, as found in [`Analyzer().incremental_outcome()`](/meridian/reference/api/meridian/analysis/analyzer/Analyzer#incremental_outcome).\n2. Use the `selected_times` option to choose the weeks of interest.\n3. Divide by spend over those weeks. This gives you the ROI and reflects the individual time period more accurately.\n\n\n**Important:** When tracking ROI over time, consider that even though the\ncoefficients in the model are not time-varying, the ROI can still change over\ntime because it is dependent on additional factors that might vary across\ntime. For example, the Hill curves model the non-linear, diminishing returns\nof media execution, and therefore the amount of media execution at a given time\ncan impact the ROI. Addtionally, media allocation can vary across\ngeos over time with different effectiveness and the cost of media\nexecution can vary across time.\n\nInterpretation and optimization\n-------------------------------\n\n#### Can I measure the ROI of bidding strategies based on the bid targets set?\n\n\nGoogle's MMM data feed provides bid strategy type (such as Maximize\nConversions and Target ROAS) by campaign, but the feed does not include the bid\ntarget itself. To view this specific dimension, advertisers can source bid\nstrategy reports directly from Google Ads or work with their Google Account\nRepresentative on a custom data solution."]]
