Module: meridian.protos

Table of Contents

• date_interval.proto

  • DateInterval

• estimate.proto

  • Estimate
  • Estimate.Uncertainty

• model_fit.proto

  • ModelFit
  • Performance
  • Prediction
  • Result

• kpi_type.proto

  • KpiType

• outcome.proto

  • Contribution
  • Effectiveness
  • Outcome

• response_curve.proto

  • ResponseCurve
  • ResponsePoint

• media_analysis.proto

  • MediaAnalysis
  • SpendInfo

• non_media_analysis.proto

  • NonMediaAnalysis

• marketing_analysis.proto

  • MarketingAnalysis
  • MarketingAnalysisList

• target_metric.proto

  • TargetMetric

• marketing_data.proto

  • ControlVariable
  • GeoInfo
  • Kpi
  • Kpi.NonRevenue
  • Kpi.Revenue
  • MarketingData
  • MarketingDataMetadata
  • MarketingDataMetadata.ChannelDimension
  • MarketingDataMetadata.GeoDimension
  • MarketingDataMetadata.TimeDimension
  • MarketingDataPoint
  • MediaVariable
  • NonMediaTreatmentVariable
  • ReachFrequencyVariable
  • ScalarMetric

• new_marketing_data.proto

  • NewMarketingData
  • NewMarketingDataPoint

• constraints.proto

  • BudgetConstraint
  • FrequencyConstraint
  • TargetMetricConstraint

• budget_optimization.proto

  • BudgetOptimization
  • BudgetOptimizationResult
  • BudgetOptimizationSpec
  • ChannelConstraint
  • FixedBudgetScenario
  • FlexibleBudgetScenario
  • IncrementalOutcomeGrid
  • IncrementalOutcomeGrid.Cell
  • IncrementalOutcomeGrid.ChannelCells

• reach_frequency_optimization.proto

  • FrequencyOutcomeGrid
  • FrequencyOutcomeGrid.Cell
  • FrequencyOutcomeGrid.ChannelCells
  • OptimizedChannelFrequency
  • ReachFrequency
  • ReachFrequencyOptimization
  • ReachFrequencyOptimizationResult
  • ReachFrequencyOptimizationSpec
  • RfChannelConstraint

• marketing_optimization.proto

  • MarketingOptimization

• mmm_kernel.proto

  • MmmKernel

• mmm.proto

  • Mmm

• eda_spec.proto

  • AggregationConfig
  • AggregationConfig.ControlVariablesEntry
  • AggregationConfig.NonMediaTreatmentsEntry
  • AggregationFunction
  • EDASpec
  • EDASpec.FunctionRegistryEntry
  • VIFSpec

• meridian_model.proto

  • AdstockDecayByChannel
  • AdstockDecayByChannel.ChannelDecaysEntry
  • Distribution
  • Distribution.BatchBroadcast
  • Distribution.Beta
  • Distribution.Bijector
  • Distribution.Bijector.Reciprocal
  • Distribution.Bijector.Scale
  • Distribution.Bijector.Shift
  • Distribution.Deterministic
  • Distribution.HalfNormal
  • Distribution.LogNormal
  • Distribution.Normal
  • Distribution.Transformed
  • Distribution.TruncatedNormal
  • Distribution.Uniform
  • Hyperparameters
  • InferenceData
  • InferenceData.AuxiliaryDataEntry
  • McmcSamplingTrace
  • MeridianModel
  • ModelConvergence
  • NonMediaBaselineValue
  • Parameter
  • PriorDistributions
  • PriorTfpDistributions
  • PriorTfpDistributions.FunctionRegistryEntry
  • RHatDiagnostic
  • TfpBijector
  • TfpBijector.ParametersEntry
  • TfpDistribution
  • TfpDistribution.ParametersEntry
  • TfpParameterValue
  • TfpParameterValue.Dict
  • TfpParameterValue.Dict.ValueMapEntry
  • TfpParameterValue.FunctionParam

  • TfpParameterValue.List

  • MediaEffectsDistribution

  • NonMediaBaselineValue.NonMediaBaselineFunction

  • NonPaidTreatmentsPriorType

  • PaidMediaPriorType

Top

date_interval.proto

DateInterval

Field	Type	Description
start_date	Date	The start date of the interval. Inclusive. Required.
end_date	Date	The end date of the interval. Exclusive. Required.
tag	string	A tag to identify the date interval. Optional.

Top

estimate.proto

Estimate

Contains an estimate value of estimand and associated quantified uncertainties.

Field	Type	Description
value	double	The estimate value of an estimand. Required.
uncertainties	Uncertainty	The quantified uncertainties.

Estimate.Uncertainty

The uncertainty of an estimate quantified by probability interval.

Field	Type	Description
probability	double	The probability that a value is inside an interval bounded by lowerbound and upperbound. Required.
lowerbound	double	The lowerbound of the interval. Required.
upperbound	double	The upperbound of the interval. Required.

Top

model_fit.proto

ModelFit

Field	Type	Description
results	Result	Different results for different purposes. For example, one could divide a data to training set, testing set, and validation set. Required.

Performance

The model fit performance indicated by different metrics.

Field	Type	Description
r_squared	double
mape	double	Mean absolute percentage error.
weighted_mape	double	Is equal to sum_i(actual_i - pred_i) / sum_i(actual_i)
rmse	double	Root mean square error.

Prediction

A prediction contains the predicted KPI and the ground truth at a specific time.

Field	Type	Description
date_interval	DateInterval	The time associated to this prediction point. Required.
predicted_outcome	Estimate	The predicted outcome. Required.
predicted_baseline	Estimate	The predicted baseline. Optional.
actual_value	double	The actual value observed in the data. Required.

Result

Field	Type	Description
name	string	The name of the result. Required.
predictions	Prediction	The predictions over different times. Required.
performance	Performance	The performance of the model fit. Required.

Top

kpi_type.proto

KpiType

Different KPI types used in marketing performance and optimization.

Name	Number	Description
KPI_TYPE_UNSPECIFIED	0
NON_REVENUE	1	Some generic user-defined KPI unit.
REVENUE	2	KPI defined as revenue specifically, or some KPI unit after conversion to revenue.

Top

outcome.proto

Contribution

A contribution is defined as the value difference caused by adding a new outcome.

Field	Type	Description
value	Estimate	The contribution value. Required.
share	Estimate	Share of contribution = contribution / total contribution from all outcomes.

Effectiveness

Effectiveness calculates how much incremental KPI is generated per media unit (e.g. impressions or clicks), i.e. contribution / media unit.

Field	Type	Description
media_unit	string	The media unit of the effectiveness. Required.
value	Estimate	The value of the effectiveness. Required.

Outcome

An outcome analysis on KPI which can be defined as revenue or other generic non-revenue type.

Field	Type	Description
kpi_type	KpiType	The type of this KPI (i.e. REVENUE or NON_REVENUE) Note that a model input with non-revenue (generic KPI) data can still have revenue-based KPI outcomes defined, provided that revenue_per_kpi is defined.
contribution	Contribution	The contribution to a KPI. If kpi_type == REVENUE , this is the revenue KPI value. If kpi_type == NON_REVENUE , AND there is a revenue_per_kpi conversion, this is the derived kpi * revenue_per_kpi value. Otherwise, this is simply the (non-revenue, user-defined) KPI value.
effectiveness	Effectiveness	The effectiveness of this outcome.
roi	Estimate	ROI = contribution / spend. See contribution definition above.
marginal_roi	Estimate	Marginal ROI shows the additional ROI gained from additional spend. See ROI definition above.
cost_per_contribution	Estimate	Cost per incremental outcome (which could be revenue or some generic KPI). E.g. when contribution is thousand impression, it is CPM; when contribution is acquisition, this is CPA.

Top

response_curve.proto

ResponseCurve

A response curve is used to show how much incremental outcome moves in response to changes to the input value (e.g. spend amount on a paid media channel, advertising impressions in a channel, etc).

Field	Type	Description
input_name	string	The name of the input. Required.
response_points	ResponsePoint	The response points. Required.

ResponsePoint

Field	Type	Description
input_value	double	The amount of the input that drives the incremental KPI. Required.
incremental_kpi	double	The incremental KPI caused by the input. Required.

Top

media_analysis.proto

MediaAnalysis

An analysis on a media channel between its spend variable and KPI(s) outcome.

Field	Type	Description
channel_name	string	The name of the media channel. Required.
spend_info	SpendInfo	The spend information of this media channel. This is optional and is left unset for a non-paid media channel analysis.
media_outcomes	Outcome	The marketing outcomes of advertising from this media channel. Required. The outcome(s) calculated based on revenue and/or generic non-revenue KPI. One or more outcome values are set when revenue and/or generic non-revenue KPI outcome information is available. For a non-paid media, the spend-related fields in Outcome won't be set.
response_curve	ResponseCurve	A response curve for the media channel. When the media is a non-paid media, response curve is not available due to the lack of spend information.

SpendInfo

Field	Type	Description
spend	double	The amount spent on the media channel. Required
spend_share	double	Spend share = spend / total spend. Required

Top

non_media_analysis.proto

NonMediaAnalysis

The analysis on non-media factor.

Field	Type	Description
non_media_name	string	The name of the non-media factor. Required.
non_media_outcomes	Outcome	The marketing outcomes from this non-media factor. Required. The non-media outcome(s) calculated based on revenue and/or generic non-revenue KPI. One or more outcome values are set when revenue and/or generic non-revenue KPI outcome information is available. The spend-related fields herein won't be set as non-media factor doesn't have spend.

Top

marketing_analysis.proto

MarketingAnalysis

The marketing analysis.

Field	Type	Description
date_interval	DateInterval	The date interval that the analysis covers. Required.
media_analyses	MediaAnalysis	Analysis on different media channels. Required.
non_media_analyses	NonMediaAnalysis	Analysis on different non-media factors.

MarketingAnalysisList

A list of marketing analyses.

Field	Type	Description
marketing_analyses	MarketingAnalysis	The marketing analyses for different time ranges. Required.

Top

target_metric.proto

TargetMetric

Target metrics for marketing performance optimizations. Note that each of these metric variants can be interpreted in terms of either revenue or non-revenue KPI type . See: common.KpiType .

Name	Number	Description
TARGET_METRIC_UNSPECIFIED	0
KPI	1	Any KPI type (revenue if the model data can be converted to revenue, or some generic KPI otherwise).
ROI	2	ROI = net KPI change / spend.
MARGINAL_ROI	3	Marginal ROI is defined at channel level and is applied across all channels. mROI = changed in incremental KPI / spend.
COST_PER_INCREMENTAL_KPI	4	CPIK = total spend / change in incremental KPI.

Top

marketing_data.proto

ControlVariable

The control variable. A control variable is not directly being studied but included in the model to account for potential confounding effects on the relationship between the primary independent and dependent variables. Examples: seasonality factors, and macroeconomic factors.

Field	Type	Description
name	string	The name of the variable. Required.
value	double	The value of the variable. Required.

GeoInfo

Field	Type	Description
geo_id	string	The ID of the geo location. Required.
population	int64	The population of the geo location. Required.

Kpi

A KPI (key performance indicator) can either be revenue directly, or some other metric that indirectly contributes to revenue eventually (e.g. sales units, conversions, impressions, etc) with a multiplier value to roughly translates said non-revenue KPI unit into revenue.

Field	Type	Description
name	string	The name of the KPI. Required.
revenue	Revenue
non_revenue	NonRevenue

Kpi.NonRevenue

A non-revenue type of KPI.

Field	Type	Description
value	double	The value of the non-revenue KPI.
revenue_per_kpi	double	Used to convert non-revenue KPI value to revenue. Needs to be non-negative.

Kpi.Revenue

A revenue KPI.

Field	Type	Description
value	double	The revenue value.

MarketingData

A collection of marketing data points for different combinations of geo locations and times, used for model training.

Field	Type	Description
marketing_data_points	MarketingDataPoint	The marketing data points. Required.
metadata	MarketingDataMetadata	Metadata support useful to recreate a model data in its domain language.

MarketingDataMetadata

Metadata support useful to validate data points and recreate a model data in its domain language.

Field	Type	Description
time_dimensions	TimeDimension	One or more sets of time coordinates. Required.
geo_dimension	GeoDimension	The geo dimension of the model. Required.
channel_dimensions	ChannelDimension	One or more sets of channel names. Required.
control_names	string	Control variable names.
kpi_type	string	The KPI type.
non_media_treatment_names	string	Non-media treatment variable names.

MarketingDataMetadata.ChannelDimension

A named set of channel name coordinates.

Field	Type	Description
name	string	A name for this set of channel names in this dimension. Optional.
channels	string	The channel names in this set's dimensional coordinates. Required.

MarketingDataMetadata.GeoDimension

Field	Type	Description
geo_coordinates	string

MarketingDataMetadata.TimeDimension

A named set of time coordinates.

Field	Type	Description
name	string	A name for this set of time coordinates. Optional.
dates	Date	The coordinates of this time dimension in the model, as dates. Required.

MarketingDataPoint

A data point contains marketing information at specific geo and time.

Field	Type	Description
geo_info	GeoInfo	Geo info of this data point. If unset, this data point is aggregated across all geos in the model's geo coordinates.
date_interval	DateInterval	Date interval covered by this data point. Required. This can represent either a coordinate point, or an aggregation over a time dimension's coordinates. In the case of the latter, this field should be defined with [start, end + interval] value, where start and end are the first and last coordinate in that time dimension, respectively.
control_variables	ControlVariable	The control variables associated to the marketing at this geo and time.
media_variables	MediaVariable	The media variables associated to the marketing at this geo and time. If a media variable is from a paid media channel and its media spend breakdown by geo and time is not available (i.e. media spend is aggregated across all geos and times), then there should be a separate MarketingDataPoint message with media_spend where geo_info is unset and date_interval spans the entire time dimension's coordinates. Media channel names should be unique across this group of media variables.
reach_frequency_variables	ReachFrequencyVariable	The reach and frequency variables associated to the marketing at this geo and time. If a reach and frequency variable is from a paid media channel and its spend breakdown by geo and time is not available (i.e. spend is aggregated across all geos and times), then there should be a separate MarketingDataPoint message with spend where geo_info is unset and date_interval spans the entire time dimension's coordinates. Reach and frequency variable names should be unique across this group of reach and frequency variables.
kpi	Kpi	The KPI associated to the marketing at this geo and time. Kpi type must be consistent across all data points.
non_media_treatment_variables	NonMediaTreatmentVariable	Non-media treatment variables associated with this data point.

MediaVariable

The media variable.

Field	Type	Description
channel_name	string	The name of the media channel. Required.
scalar_metric	ScalarMetric	Scalar metric measured on the channel. Required.
media_spend	double	Spend on the media. If the media variable is a paid media, spend is required.

NonMediaTreatmentVariable

The non-media treatment variable. A marketing activity that is not directly related to media, such as running a promotion, the price of a product, and a change in a product's packaging or design.

Field	Type	Description
name	string	The name of the variable. Required.
value	double	The value of the variable. Required.

ReachFrequencyVariable

The reach and frequency variable.

Field	Type	Description
channel_name	string	The name of the reach and frequency variable. Required.
reach	int64	The reach value. Required.
average_frequency	double	The average frequency value. Required.
spend	double	The spend value. If the reach and frequency variable is a paid media, spend is required.

ScalarMetric

Scalar type of metrics. Ex: Impressions, clicks, costs, and etc.

Field	Type	Description
name	string	The name of the scalar metric. Required.
value	double	The value of the scalar metric. Required.

Top

new_marketing_data.proto

NewMarketingData

A collection of independent marketing data points for different combinations of geo locations and times, used to override training data for model inference. This can span any time period, including times overlapping with and beyond the modeling period.

Field	Type	Description
marketing_data_points	NewMarketingDataPoint	The independent marketing data points for each geo and time.

NewMarketingDataPoint

A new marketing data point used for model inference. This contains independent marketing data at a specific geo and time.

Field	Type	Description
geo_info	GeoInfo	Geo info of this data point. Required for a geo model.
date_interval	DateInterval	Date interval covered by this data point. This can represent either a coordinate point, or an aggregation over a time dimension's coordinates. In the case of the latter, this field should be defined with [start, end + interval] value, where start and end are the first and last coordinate in that time dimension, respectively.
media_variables	MediaVariable	The media variables associated with the marketing at this geo and time.
reach_frequency_variables	ReachFrequencyVariable	The reach and frequency variables associated with the marketing at this geo and time.
revenue_per_kpi	double	The revenue per KPI associated with the marketing at this geo and time. Required for revenue analysis.

Top

constraints.proto

BudgetConstraint

Field	Type	Description
min_budget	double	Required. Absolute minimum budget value.
max_budget	double	Required. Absolute maximum budget value.

FrequencyConstraint

Field	Type	Description
min_frequency	double	Required.
max_frequency	double	Required.

TargetMetricConstraint

The constraint of target metrics.

Field	Type	Description
target_metric	TargetMetric	The type of the target metric that is constrained. Required.
target_value	double	The constraint on the target metric value. Required. Whether this target value represents a lower or upper bound depends on the target metric set above.

Top

budget_optimization.proto

BudgetOptimization

Field	Type	Description
results	BudgetOptimizationResult	Optimization results for different scenarios.

BudgetOptimizationResult

The budget optimization finds the result of optimal budget allocation given an optimization spec.

Field	Type	Description
name	string	The name of the budget optimization. Required.
group_id	string	An optional identifier for a result that belongs to a group of related results (of different types). Note that no two BudgetOptimizationResult s should share the same group ID Simple UUID strings are recommended.
spec	BudgetOptimizationSpec	The optimization spec used to generate the result. Required.
optimized_marketing_analysis	MarketingAnalysis	The analysis on marketing outcome when using the optimized budget. Required. The non-media outcomes are not optimized, but some fields might be impacted by the change of media outcomes. For example, total contribution would change, so the contribution share values have to modified accordingly.
nonoptimized_marketing_analysis	MarketingAnalysis	The non-optimized marketing outcome. Required. In a fixed budget scenario, non-optimized marketing outcome is based on the budget amount. In a flexible budget scenario, the outcome is based on the historical spend.
incremental_outcome_grid	IncrementalOutcomeGrid	Optional search grid that describes incremental outcomes of spends on channels. Useful for speeding up optimization analysis.

BudgetOptimizationSpec

Input to the optimizer.

Field	Type	Description
date_interval	DateInterval	The date interval defines the selection of the time points that the optimization is based upon.
objective	TargetMetric	The objective to maximize in the budget optimization. Required.
kpi_type	KpiType	The type of KPI used to derive the optimization objective. Required.
new_marketing_data	NewMarketingData	The new marketing data to override the flighting pattern and CPM. If not provided, the optimization will be based on the historical data.
use_optimal_frequency	bool	Whether to use the optimal frequency, If set to False , max_frequency is ignored.
max_frequency	float	The max frequency to use for the optimal frequency search space, this field is ignored if use_optimal_frequency is unset or is set to False . If not set when use_optimal_frequency is set to True , the max frequency of the input data is used. max_frequency must be greater than 1 if set.
fixed_budget_scenario	FixedBudgetScenario	A fixed budget optimization tries to maximize an objective by optimizing the budget allocations over channels without changing the total budget amount. For instance, in Meridian, the objective function is chosen to be the posterior mean of the expected KPI (e.g. sales, revenue, etc).
flexible_budget_scenario	FlexibleBudgetScenario	A flexible budget optimization tries to maximize an objective by optimizing the budget allocations over channels with a flexible total budget amount. For instance, in Meridian, the expected KPI (e.g. revenue) can be optimized while allowing the total budget to vary: this flexible optimization in Meridian is constrained on either the minimal marginal ROI or the target ROI constraints.
channel_constraints	ChannelConstraint	The constraints on channels. If a media channel that is present in the model is not represented here, it will be given the default constraint of [0, max_budget] .

ChannelConstraint

Channel level constraint

Field	Type	Description
channel_name	string	The name of the channel. Required.
budget_constraint	BudgetConstraint	The budget constraint on the channel.

FixedBudgetScenario

A fixed budget scenario for optimizing budget allocations over channels.

Field	Type	Description
total_budget	double	The budget amount. Required.

FlexibleBudgetScenario

A flexible budget scenario for optimizing budget allocations over channels.

Field	Type	Description
total_budget_constraint	BudgetConstraint	The constraint parameters on the total budget.
target_metric_constraints	TargetMetricConstraint	The constraints on target metrics (e.g. KPI, ROI, etc).

IncrementalOutcomeGrid

A message representing a grid that details the incremental outcome of marketing spend by channel.

Note that this grid is constructed under the assumption that there is no interaction effect across channels, i.e. the spend on one channel will not affect other channels.

Field	Type	Description
name	string	The name of the grid. Required.
spend_step_size	double	The uniform step size between consecutive spend values within a channel. Required.
channel_cells	ChannelCells	The collection of cells representing all combinations of spend and incremental outcome across channels. Required. Each channel can have a different spend range, but all spend values within a channel must be spaced evenly using the specified step size.

IncrementalOutcomeGrid.Cell

A data point within the grid representing the outcome of a specific spend on a particular channel.

Field	Type	Description
spend	double	The amount of marketing spend allocated to the channel.
incremental_outcome	Estimate	The incremental outcome achieved through the channel spend. The type should be indicated by the objective in the optimization spec. This is calculated as the difference between the outcome with the given spend and the outcome with zero spend (outcome(spend) - outcome(0)).

IncrementalOutcomeGrid.ChannelCells

A collection of cells in a channel.

Field	Type	Description
channel_name	string	The name of the marketing channel.
cells	Cell	The cells in the channel.

Top

reach_frequency_optimization.proto

FrequencyOutcomeGrid

A message representing a grid that details the outcome of reach frequency by channel.

Note that this grid is constructed under the assumption that there is no interaction effect across channels, i.e. the reach frequency on one channel will not affect other channels.

Field	Type	Description
name	string	The name of the grid. Required.
frequency_step_size	double	The uniform step size between consecutive frequency values within a channel. Required.
channel_cells	ChannelCells	The collection of cells representing all combinations of reach frequency and outcome across channels. Required.

FrequencyOutcomeGrid.Cell

A data point within the grid representing the outcome of a specific reach frequency on a particular channel.

Field	Type	Description
reach_frequency	ReachFrequency	The reach and frequency of the channel.
outcome	Estimate	The outcome achieved through the channel reach frequency. The type should be indicated by the objective in the optimization spec.

FrequencyOutcomeGrid.ChannelCells

A collection of cells in a channel.

Field	Type	Description
channel_name	string	The name of the marketing channel.
cells	Cell	The cells in the channel.

OptimizedChannelFrequency

Reach frequency optimization result for channel that has reach frequency information.

Field	Type	Description
channel_name	string	The name of the channel. Required.
optimal_average_frequency	double	The optimal average frequency of the channel. Required.

ReachFrequency

Reach and frequency metric.

Field	Type	Description
reach	int64	Reach value. Required.
average_frequency	double	Average frequency value. Required.

ReachFrequencyOptimization

Field	Type	Description
results	ReachFrequencyOptimizationResult	Optimization results for different scenarios.

ReachFrequencyOptimizationResult

Field	Type	Description
name	string	The name of the reach frequency optimization. Required.
group_id	string	An optional identifier for a result that belongs to a group of related results (of different types). Note that no two ReachFrequencyOptimizationResult s should share the same group ID. Simple UUID strings are recommended.
spec	ReachFrequencyOptimizationSpec	The optimization spec used to generate the result. Required.
optimized_channel_frequencies	OptimizedChannelFrequency	Optimal average frequency results by channel. Required. Media channels without reach frequency data won't be shown.
optimized_marketing_analysis	MarketingAnalysis	The analysis on marketing outcome when using the optimal channel frequencies. Required. The non-media outcomes are not optimized, but some fields might be impacted by the change of media outcomes. For example, total contribution would change, so the contribution share values have to modified accordingly.
frequency_outcome_grid	FrequencyOutcomeGrid	Optional grid that describes the outcomes of reach frequency on channels.

ReachFrequencyOptimizationSpec

Field	Type	Description
date_interval	DateInterval	The date interval defines the selection of the time points that the optimization is based upon.
objective	TargetMetric	The objective to maximize in the reach frequency optimization. Required.
kpi_type	KpiType	The type of KPI used to derive the optimization objective. Required.
target_metric_constraints	TargetMetricConstraint	The constraints on target metrics (e.g. KPI, ROI, etc).
total_budget_constraint	BudgetConstraint	The constraint on the total budget.
rf_channel_constraints	RfChannelConstraint	The constraints on channels that have reach frequency information.

RfChannelConstraint

Channel level constraint for channel that has reach frequency information.

Field	Type	Description
channel_name	string	The name of the channel. Required.
budget_constraint	BudgetConstraint	The budget constraint on the channel.
frequency_constraint	FrequencyConstraint	The frequency constraint on the channel.

Top

marketing_optimization.proto

MarketingOptimization

Marketing optimization contains all optimization related results.

Field	Type	Description
budget_optimization	BudgetOptimization	Budget optimization that contains results for different scenarios.
reach_frequency_optimization	ReachFrequencyOptimization	Reach frequency optimization that contains results for different scenarios.

Top

mmm_kernel.proto

MmmKernel

Field	Type	Description
marketing_data	MarketingData	The marketing data that is used to train the marketing mix model and later analyzed by the model.
model	Any	The details about the model implementation. This should contain a trained marketing mix model along with model-specific information such as model convergence, flags of model usage.

Top

mmm.proto

Mmm

A schema that contains derived metrics and modeled analysis by a trained marketing mix model.

Field	Type	Description
mmm_kernel	MmmKernel	A MMM kernel contains the core information about the model used to generate this output.
model_fit	ModelFit	Model fit result.
marketing_analysis_list	MarketingAnalysisList	A list of marketing analysis generated by the MMM kernel.
marketing_optimization	MarketingOptimization	Marketing optimization on different perspectives using the MMM kernel.

Top

eda_spec.proto

AggregationConfig

A configuration for customizing variable aggregation functions.

Variables not specified will default to SUM.

Field	Type	Description
control_variables	ControlVariablesEntry	A map from control variable names to their aggregation functions.
non_media_treatments	NonMediaTreatmentsEntry	A map from non-media treatment names to their aggregation functions.

AggregationConfig.ControlVariablesEntry

Field	Type	Description
key	string
value	AggregationFunction

AggregationConfig.NonMediaTreatmentsEntry

Field	Type	Description
key	string
value	AggregationFunction

AggregationFunction

Represents an aggregation function used in EDA.

Field	Type	Description
function_key	string	A key that maps to a custom function in the user-provided function registry. The registry allows the EDA spec to be serialized without including the function's code, enabling a more secure deserialization process. Required.

EDASpec

A container for all user-configurable EDA check specs.

Field	Type	Description
aggregation_config	AggregationConfig	Configuration for custom variable aggregation functions.
vif_spec	VIFSpec	Specification for the EDA VIF check.
function_registry	FunctionRegistryEntry	Lookup table that contains function names mapped to hashed functions used by various aggregation functions.

EDASpec.FunctionRegistryEntry

Field	Type	Description
key	string
value	string

VIFSpec

A spec for the EDA VIF check.

Field	Type	Description
geo_threshold	double	The threshold for geo-level VIF. (Python default: 1000.0)
overall_threshold	double	The threshold for overall VIF. (Python default: 1000.0)
national_threshold	double	The threshold for national VIF. (Python default: 1000.0)

Top

meridian_model.proto

AdstockDecayByChannel

Specifies the adstock decay function for each channel.

Field	Type	Description
channel_decays	ChannelDecaysEntry	A map where keys are channel names and values are the adstock decay function to use for that channel. Allowed values are 'geometric' or 'binomial'.

AdstockDecayByChannel.ChannelDecaysEntry

Field	Type	Description
key	string
value	string

Distribution

Represents Tensorflow statistical distributions that are used in user priors in a Meridian model. All fields are required unless otherwise specified. See: https://www.tensorflow.org/probability/api_docs/python/tfp/distributions

Field	Type	Description
name	string	The canonical name of this distribution in the Meridian model framework.
batch_broadcast	BatchBroadcast
deterministic	Deterministic
half_normal	HalfNormal
log_normal	LogNormal
normal	Normal
transformed	Transformed
truncated_normal	TruncatedNormal
uniform	Uniform
beta	Beta

Distribution.BatchBroadcast

A distribution that broadcasts an underlying distribution's batch shape.

Field	Type	Description
distribution	Distribution	The underlying (pre-broadcast) distribution.
batch_shape	TensorShapeProto	The shape of the broadcast distribution.

Distribution.Beta

A Beta distribution with alpha and beta parameters. See: https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Beta

Field	Type	Description
alpha	double	The alpha parameter of the underlying Beta distribution.
beta	double	The beta parameter of the underlying Beta distribution.

Distribution.Bijector

Represents Tensorflow bijectors. All fields are required unless otherwise specified. See: https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors

Field	Type	Description
name	string	The name of this bijector.
shift	Shift
scale	Scale
reciprocal	Reciprocal

Distribution.Bijector.Reciprocal

A bijector that reciprocates the input.

Distribution.Bijector.Scale

A bijector that scales the input by a scalar or log scale.

Field	Type	Description
scales	double	The scale to apply to the input. Should be not be set if log_scales is set.
log_scales	double	The log scale to apply to the input. Should be not be set if scales is set.

Distribution.Bijector.Shift

A bijector that shifts the input by a scalar.

Field	Type	Description
shifts	double	The shift to apply to the input.

Distribution.Deterministic

A scalar deterministic distribution on the real line.

Field	Type	Description
locs	double	The batch of points on which this distribution is supported.

Distribution.HalfNormal

A half-normal distribution with scales.

Field	Type	Description
scales	double	The scales of the distribution.

Distribution.LogNormal

A log-normal distribution with locs (means) and scales (stddevs).

Field	Type	Description
locs	double	The means of the underlying Normal distribution.
scales	double	The standard deviations of the underlying normal distribution.

Distribution.Normal

A normal distribution with locs (means) and scales (stddevs).

Field	Type	Description
locs	double	The means of the underlying Normal distribution.
scales	double	The standard deviations of the underlying normal distribution. Must contain only positive values.

Distribution.Transformed

A distribution that is transformed by a bijector.

Field	Type	Description
distribution	Distribution	The underlying (pre-transformed) distribution.
bijector	Bijector	The transforming bijector.

Distribution.TruncatedNormal

A truncated Normal distribution, bounded between low and high .

Field	Type	Description
locs	double	The means of the underlying Normal distribution.
scales	double	The standard deviations of the underlying Normal distribution.
low	double	Lower bound of the distribution's support. Must be less than high .
high	double	Upper bound of the distribution's support. Must be greater than low .
lows	double	Lower bounds of the distribution's support. Each value in lows must be less than the corresponding value in highs .
highs	double	Upper bounds of the distribution's support. Each value in highs must be greater than the corresponding value in lows .

Distribution.Uniform

A uniform distribution on the real line.

Field	Type	Description
low	double	Lower boundary of the output interval. Must be less than high .
high	double	Upper boundary of the output interval. Must be greater than low .
lows	double	Lower boundaries of the output interval. Each value in lows must be less than the corresponding value in highs .
highs	double	Upper boundaries of the output interval. Each value in highs must be greater than the corresponding value in lows .

Hyperparameters

Hyperparameters for the MMM model.

Field	Type	Description
media_effects_dist	MediaEffectsDistribution	Specifies the distribution of media random effects across geos. This hyperparameter is ignored in a national-level model.
hill_before_adstock	bool	Indicates whether to apply the Hill function before Adstock function. This hyperparameter does not apply to RF channels.
max_lag	uint32	The maximum number of lag periods (>= 0) to include in the Adstock calculation. If unset, then max lag is interpreted as infinite.
unique_sigma_for_each_geo	bool	Indicates whether to use a unique residual variance for each geo. If False, then a single residual variance is used for all geos.
paid_media_prior_type	PaidMediaPriorType	Prior type for the media coefficients. If paid_media_prior_type is 'coefficient' , then the model uses beta_[m
media_prior_type	PaidMediaPriorType	Prior type for the (paid, non-rf) media coefficients. If media_prior_type is 'coefficient' , then the model uses beta_m distribution in the priors. If media_prior_type' is 'roi' or 'mroi' , then the roi_m or mroi_m is used, respectively. If media_prior_type is 'contribution' , then the contribution_m is used.
rf_prior_type	PaidMediaPriorType	Prior type for the (paid) rf coefficients. If rf_prior_type is 'coefficient' , then the model uses beta_rf distribution in the priors. If rfprior_type' is 'roi' or 'mroi' , then the roi_rf or mroi_rf is used, respectively. If rf_prior_type is 'contribution' , then the contribution_rf is used.
organic_media_prior_type	NonPaidTreatmentsPriorType	Prior type for the organic media coefficients. If organic_media_prior_type is 'coefficient' , then the model uses beta_om distribution in the priors. If organic_media_prior_type is 'contribution' , then the contribution_om` is used.
organic_rf_prior_type	NonPaidTreatmentsPriorType	Prior type for the organic rf coefficients. If organic_rf_prior_type is 'coefficient' , then the model uses beta_orf distribution in the priors. If organic_rf_prior_type is 'contribution' , then the contribution_orf` is used.
non_media_treatments_prior_type	NonPaidTreatmentsPriorType	Prior type for the non-media treatments coefficients. If non_media_treatments_prior_type is 'coefficient' , then the model uses gamma_n distribution in the priors. If non_media_treatments_prior_type is 'contribution' , then the contribution_n` is used.
roi_calibration_period	TensorProto	A boolean tensor in the shape (n_media_times, n_media_channels) . This indicates the subset of time coordinates in the model for media ROI calibration. If unset, all time coordinates are used for media ROI calibration.
rf_roi_calibration_period	TensorProto	A boolean tensor in the shape (n_media_times, n_rf_channels) . This indicates the subset of time coordinates in the model for reach and frequency ROI calibration. If unset, all time coordinates are used for R&F ROI calibration.
knots	int32	A (single-value) integer or a list of integers, indicating the knots used to estimate time effects. If provided as a list of integers, its indices correspond to the indices of the time coordinates in the model. If provided as a single integer, then there are knots with locations equally spaced across time periods. If unset, then the number of knots used is equal to the number of time periods in the case of a geo model (i.e. each time period has its own regression coefficient). If unset in a national model, then the model uses 1 as the number of knots.
enable_aks	bool	A boolean indicating whether to use the Automatic Knot Selection algorithm to select optimal number of knots for running the model instead of the default 1 for national and n_times for non-national models. If this is set to true and the knots arg is provided, then an error will be raised when deserialized back to ModelSpec. Default: False .
baseline_geo_int_deprecated	double	Deprecated. Use baseline_geo_int instead.
baseline_geo_int	int32	Integer representation of baseline geo.
baseline_geo_string	string
holdout_id	TensorProto	A boolean tensor in the shape (n_geos, n_times) for a geo-level model or (n_times,) for a national model. This indicates which observations are part of the holdout sample, which are excluded from the training sample. For more details on the holdout sample, see: meridian.model.spec.ModelSpec
holdout_ratio	double	The ratio of holdout data to use for the goodness of fit check. Used as inputs and this will be implemented in training module right before feeding into Meridian. The holdout only applied to date as of Q1 2025.
control_population_scaling_id	TensorProto	A boolean tensor in the shape (n_controls,) . This indicates the control variables for which the control value will be scaled by population. If unset, no control variables are scaled by population.
non_media_population_scaling_id	TensorProto	A boolean tensor in the shape (n_non_media_channels,) . This indicates the non-media treatments channels for which the value will be scaled by population. If unset, no non-media treatments channels are scaled by population.
global_adstock_decay	string	The global adstock decay function to use for all channels. Allowed values are 'geometric' or 'binomial'.
adstock_decay_by_channel	AdstockDecayByChannel	Channel-specific adstock decay functions. Defaults to 'geometric' for channels not specified in the map.
non_media_baseline_values	NonMediaBaselineValue	A list with the shape (n_non_media_channels,) . Each element is either a float (which means that the fixed value will be used as baseline for the given channel) or one of the function strings "min" or "max" (which mean that the global minimum or maximum value will be used as baseline for the scaled values of the given non_media treatments channel). If None , the minimum value is used as baseline for each non-media treatments channel. This attribute is used as the default value for the corresponding argument to Analyzer methods.

InferenceData

InferenceData data contains none, only prior, or both prior and posterior sampled parameters and their sampling states and trace from fitting the model. https://python.arviz.org/en/stable/api/generated/arviz.InferenceData.html

All fields inside this container are xarray.Dataset s that are byte-serialized in NetCDF format. See: https://docs.xarray.dev/en/stable/user-guide/io.html

Field	Type	Description
prior	bytes	Sampled prior parameters as an xarray.Dataset serialized in NetCDF4 format.
posterior	bytes	Sampled posterior parameters as an xarray.Dataset serialized in NetCDF4 format.
auxiliary_data	AuxiliaryDataEntry	Contains "sample_stats", "trace", and other auxiliary data that are useful for debugging. "sample_stats" and "trace" specifically are available when posterior is available.

InferenceData.AuxiliaryDataEntry

Field	Type	Description
key	string
value	bytes

McmcSamplingTrace

The trace of MCMC sampling.

Field	Type	Description
num_chains	uint32
num_draws	uint32
step_size	TensorProto
tune	TensorProto
target_log_prob	TensorProto
diverging	TensorProto
accept_ratio	TensorProto
n_steps	TensorProto
is_accepted	TensorProto

MeridianModel

Meridian model schema.

Field	Type	Description
model_id	string	The unique identifier of this model.
model_version	string	The semantic version of the Meridian library used to generate this model.
hyperparameters	Hyperparameters
prior_distributions	PriorDistributions
prior_tfp_distributions	PriorTfpDistributions
inference_data	InferenceData	Inference data contains sampled priors and posteriors.
convergence_info	ModelConvergence	Contains the information about model convergence status.
eda_spec	EDASpec	User-configurable EDA check specs.

ModelConvergence

Field	Type	Description
mcmc_sampling_trace	McmcSamplingTrace
convergence	bool	Convergence heuristic check for the MCMC sampling.
r_hat_diagnostic	RHatDiagnostic

NonMediaBaselineValue

A container for non-media baseline function or float values. If float, then that value is used as the baseline for all non-media channels. Otherwise, the min or max value is used as the baseline for the corresponding non-media channel.

Field	Type	Description
value	float
function_value	NonMediaBaselineFunction

Parameter

A named tensor parameter.

Field	Type	Description
name	string
tensor	TensorProto

PriorDistributions

A container for user prior distribution parameters in a Meridian model. These distributions are in their mathematical forms when representing user priors in the model spec and are part of the user input in a pre-fitted model.

After priors sampling, these distributions are broadcast and should all contain Distribution.BatchBroadcast types.

All parameter distributions are optional. If a distribution is left unspecified for a given parameter, Meridian will select its default prior distribution.

See: meridian.model.prior_distribution module. See: https://developers.google.com/meridian/docs/advanced-modeling/default-prior-distributions

Field	Type	Description
knot_values	Distribution
tau_g_excl_baseline	Distribution
beta_m	Distribution
beta_rf	Distribution
eta_m	Distribution
eta_rf	Distribution
gamma_c	Distribution
xi_c	Distribution
alpha_m	Distribution
alpha_rf	Distribution
ec_m	Distribution
ec_rf	Distribution
slope_m	Distribution
slope_rf	Distribution
sigma	Distribution
roi_m	Distribution
roi_rf	Distribution
mroi_m	Distribution
mroi_rf	Distribution
contribution_m	Distribution
contribution_rf	Distribution
contribution_om	Distribution
contribution_orf	Distribution
contribution_n	Distribution
beta_om	Distribution
beta_orf	Distribution
eta_om	Distribution
eta_orf	Distribution
gamma_n	Distribution
xi_n	Distribution
alpha_om	Distribution
alpha_orf	Distribution
ec_om	Distribution
ec_orf	Distribution
slope_om	Distribution
slope_orf	Distribution

PriorTfpDistributions

A container for user prior distribution parameters in a Meridian model. These distributions are in their mathematical forms when representing user priors in the model spec and are part of the user input in a pre-fitted model.

After priors sampling, these distributions are broadcast and should all contain Distribution.BatchBroadcast types.

All parameter distributions are optional. If a distribution is left unspecified for a given parameter, Meridian will select its default prior distribution.

See: meridian.model.prior_distribution module. See: https://developers.google.com/meridian/docs/advanced-modeling/default-prior-distributions

Field	Type	Description
knot_values	TfpDistribution
tau_g_excl_baseline	TfpDistribution
beta_m	TfpDistribution
beta_rf	TfpDistribution
eta_m	TfpDistribution
eta_rf	TfpDistribution
gamma_c	TfpDistribution
xi_c	TfpDistribution
alpha_m	TfpDistribution
alpha_rf	TfpDistribution
ec_m	TfpDistribution
ec_rf	TfpDistribution
slope_m	TfpDistribution
slope_rf	TfpDistribution
sigma	TfpDistribution
roi_m	TfpDistribution
roi_rf	TfpDistribution
mroi_m	TfpDistribution
mroi_rf	TfpDistribution
contribution_m	TfpDistribution
contribution_rf	TfpDistribution
contribution_om	TfpDistribution
contribution_orf	TfpDistribution
contribution_n	TfpDistribution
beta_om	TfpDistribution
beta_orf	TfpDistribution
eta_om	TfpDistribution
eta_orf	TfpDistribution
gamma_n	TfpDistribution
xi_n	TfpDistribution
alpha_om	TfpDistribution
alpha_orf	TfpDistribution
ec_om	TfpDistribution
ec_orf	TfpDistribution
slope_om	TfpDistribution
slope_orf	TfpDistribution
function_registry	FunctionRegistryEntry	Lookup table that contains function names mapped to hashed functions used by various tfp.distributions .

PriorTfpDistributions.FunctionRegistryEntry

Field	Type	Description
key	string
value	string

RHatDiagnostic

Diagnostic of MCMC sampling by computing r_hat value for each parameters.

Field	Type	Description
parameter_r_hats	Parameter	The r-hat values of model parameters. Current list of parameters: (see InferenceData.parameters above)

TfpBijector

Represents a constructor parameter for a tfp.bijectors class. All fields are required unless otherwise specified. See: https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors

Field	Type	Description
bijector_type	string	A tfp.bijectors class name. e.g. "Shift", "Scale", etc.
parameters	ParametersEntry	Parameters for the specific bijector type.

TfpBijector.ParametersEntry

Field	Type	Description
key	string
value	TfpParameterValue

TfpDistribution

Represents TensorFlow statistical distribution spec that are used in user priors in a Meridian model. All fields are required unless otherwise specified. See: https://www.tensorflow.org/probability/api_docs/python/tfp/distributions

Field	Type	Description
distribution_type	string	A tfp.distributions class name. e.g. "Normal", "TransformedDistribution", etc.
parameters	ParametersEntry	Parameters for the specific distribution type.

TfpDistribution.ParametersEntry

Field	Type	Description
key	string
value	TfpParameterValue

TfpParameterValue

Represents a constructor parameter for a tfp.distributions class.

Field	Type	Description
scalar_value	float	Primitive distribution parameter value types.
int_value	int32
bool_value	bool
string_value	string
none_value	bool
distribution_value	TfpDistribution	For nested distribution parameter (e.g. for TransformedDistribution )
bijector_value	TfpBijector	For nested bijector parameter (e.g. for TransformedDistribution )
list_value	List	For a parameter that takes a list of parameters.
dict_value	Dict	For a parameter that takes a dict.
tensor_value	TensorProto	For a parameter that takes a Tensor.
fully_reparameterized	bool	Whether the distribution should be fully reparameterized. See: https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/ReparameterizationType
function_param	FunctionParam	For a parameter that takes a function.

TfpParameterValue.Dict

For parameter values that are dicts.

Field	Type	Description
value_map	ValueMapEntry

TfpParameterValue.Dict.ValueMapEntry

Field	Type	Description
key	string
value	TfpParameterValue

TfpParameterValue.FunctionParam

For parameter values that are functions.

Field	Type	Description
function_key	string	A key that maps to a custom function in the user-provided function registry. The registry allows the model to be serialized without including the function's code, enabling a more secure deserialization process.
uses_default	bool	Whether the Distribution uses the default function implementation.

TfpParameterValue.List

For parameter values that are lists or tuples.

Field	Type	Description
values	TfpParameterValue

MediaEffectsDistribution

Possible distribution types for media random effects across geos.

Name	Number	Description
MEDIA_EFFECTS_DISTRIBUTION_UNSPECIFIED	0
NORMAL	1
LOG_NORMAL	2

NonMediaBaselineValue.NonMediaBaselineFunction

Possible non-media baseline function values. If used, then the min or max value of the non-media treatment is used as the baseline for the corresponding non-media channel instead of a fixed, user-specified value.

Name	Number	Description
NON_MEDIA_BASELINE_FUNCTION_UNSPECIFIED	0
MIN	1
MAX	2

NonPaidTreatmentsPriorType

Possible non-paid treatments prior types.

Name	Number	Description
NON_PAID_TREATMENTS_PRIOR_TYPE_UNSPECIFIED	0
NON_PAID_TREATMENTS_PRIOR_TYPE_COEFFICIENT	1
NON_PAID_TREATMENTS_PRIOR_TYPE_CONTRIBUTION	2

PaidMediaPriorType

Possible paid media prior types.

Name	Number	Description
PAID_MEDIA_PRIOR_TYPE_UNSPECIFIED	0
ROI	1
MROI	2
COEFFICIENT	3
CONTRIBUTION	4