This page provides definitions for key terms used throughout the Meridian
documentation.

## Baseline

The expected outcome under the counterfactual scenario where all treatment
variables are set to their baseline values. For paid and organic media, the
baseline values are zero. For non-media treatment variables, the baseline value
can be set to the observed minimum value of the variable (default), the maximum,
or a user-provided float.

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## Brand equity variable

An intermediate brand health or demand variable, such as Branded Google Query
Volume (bGQV) or brand consideration, driven by
[brand marketing (aka upper-funnel marketing)](https://developers.google.com/meridian/docs/basics/glossary#brand-marketing).

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## Brand marketing (aka upper-funnel marketing)

Marketing that drives [brand equity](https://developers.google.com/meridian/docs/basics/glossary#brand-equity-variable).

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## Calibration

The process of using experiment results and other domain knowledge to set
channel-specific priors in Meridian.

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## Contribution

Each treatment variable's incremental outcome as a percent of total outcome. For
reporting purposes (such as in
[`ModelFit`](https://developers.google.com/meridian/reference/api/meridian/analysis/visualizer/ModelFit)
and
[`MediaSummary`](https://developers.google.com/meridian/reference/api/meridian/analysis/visualizer/MediaSummary#plot_contribution_waterfall_chart)),
total outcome is total *expected* outcome. For the
purposes of [contribution
priors](https://developers.google.com/meridian/docs/advanced-modeling/roi-mroi-contribution-parameterizations#contribution),
total outcome is total *observed* outcome.

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## Control variables

Variables in the model that aren't treatment variables. Control variables are
used to estimate baseline outcome, and it is not possible to estimate causal
effects or contribution percentages for control variables. See [Control
Variables](https://developers.google.com/meridian/docs/advanced-modeling/control-variables) for important
practical advice on selecting controls. Also, see the related concept *Mediator
variables*.

### Confounding variables

Variables that have a causal effect on both the treatment and the KPI. Including
these as control variables debiases the causal estimates of the treatment on
the KPI.

### Predictor variables

Variables that have a causal effect on the KPI, but nothing else. Including
these as control variables does nothing to debias the causal estimates of the
treatment on the KPI. However, strong predictors can reduce the variance of
causal estimates.

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## Cost Per Incremental KPI (CPIK)

Total spend divided by total incremental KPI. When the KPI is not revenue and
revenue per KPI data is not available, then CPIK equals one over ROI.

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## Effect Window

The time period from the current timestep to the maximum lag, inclusive.

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## Effectiveness

Incremental outcome divided by total media units.

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## Expected outcome

The expected outcome when all treatment variables are set to actual historical
values. This is the sum total of baseline outcome plus the incremental outcome
of all treatment variables.

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## Flighting pattern

The relative distribution of media units across geographic regions and time
periods for a given media variable. This is used to allocate media units across
geographic regions and time periods when the total budget of a channel is scaled
up or down, which applies to budget optimization and response curves.

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## Incremental outcome

The change in expected outcome driven by each treatment variable. For paid and
organic media, this is the change in expected outcome when one variable is set
to zero. For non-media treatment variables, this is the change in expected
outcome when a variable is set to its baseline value (observed minimum value of
the variable (default), the maximum, or a user-provided float) for every
geographic region and time period. See [Incremental
Outcome](https://developers.google.com/meridian/docs/post-modeling/roi-mroi-response-curves#incremental_outcome)
for details.

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## KPI

The response (target, dependent) variable of the model. It can be revenue, sales
units, conversions, or anything else that the treatment variables may have a
causal effect upon.

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## Lagged effect

A causal effect of treatment variables from previous time periods affecting the
outcome in a later time period. Meridian models lagged effects using an adstock
function.

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## Marginal ROI (mROI)

The derivative of the response curve and is approximately the ROI on the next
monetary unit (such as dollar) spent beyond current spend level.

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## Media execution

A general term referring to the media unit values of a given channel across
geographic regions and time periods.

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## Mediator variables

Variables that are causally affected by the treatment and have a causal effect
on the KPI. Including these as control variables causes a bias in causal
estimates of the treatment on the KPI. They should be excluded from a standard
single-stage model. Alternatively, you can include and model these variables
using a two-stage approach like [Full-Funnel
MMM](https://developers.google.com/meridian/docs/advanced-modeling/full-funnel).

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## Outcome

The primary metric of interest that Meridian measures the causal effect of
treatment variables upon. This is typically revenue, but when the KPI is not
revenue and revenue per KPI data is not available, then Meridian defines the
outcome to be the KPI itself. It is not necessarily the response variable of the
model (see the KPI definition).

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## Response curve

A plot of incremental outcome versus spend level for a given media variable. As
the spend varies, media units are allocated across geographic regions and time
periods according to the flighting pattern.

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## Return on Investment (ROI)

Meridian defines ROI as incremental outcome divided by spend. When the KPI is
revenue or revenue per KPI data is available, the incremental outcome is
incremental revenue. Otherwise, the incremental outcome is incremental KPI.

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## Revenue

For non-revenue KPIs, this is the revenue per KPI multiplied by the KPI. For
revenue KPIs, this is the same as the KPI. When the KPI is not revenue and
revenue per KPI data is not available, revenue is undefined.

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## Revenue per KPI

The assumed revenue generated per KPI unit. This can vary by time, geographic
region, or both. Meridian multiplies incremental KPI units by the revenue per
KPI to estimate incremental revenue of the treatment variables.

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## Saturation

Meridian assumes that paid and organic media have diminishing marginal returns,
and that there is an asymptotic limit on the media effect over a given time
period. As spending increases along the response curve, the mROI diminishes. As
the spending becomes large and mROI becomes small, a channel is considered to
have become *saturated* . *Saturation* is a general term, and no specific
threshold is defined.

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## Time effects

Meridian captures time effects using time-varying intercepts ($\\mu_t$). Time
effects account for systematic variations in the KPI that occur over time,
helping causal effect estimation of the treatment variables.

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## Treatment variables

Includes all variables for which the MMM estimates a causal effect, namely paid
media, organic media, and non-media treatments. The term *treatment* comes from
the field of causal inference and is elsewhere often used synonymously with
*intervention* or *exposure*.

### Paid media variables

Includes all media channels for which spend data is available. This includes
both channels modeled with a single variable (for example, spend, impressions,
or clicks) and channels modeled with reach and frequency data.

### Organic media variables

Includes all media channels that don't have an associated cost, or where the
cost is unknown. These channels are modeled with adstock and diminishing
returns, just like paid media. The primary difference is that ROI and mROI
cannot be measured for organic channels, and so ROI and mROI priors cannot be
used for organic channels. This includes both organic media channels modeled
with a single variable (for example, spend, impressions, or clicks) and
channels modeled with reach and frequency data.

### Non-media treatment variables

Includes any non-media tactics such as pricing and promotions. These are
variables for which Meridian estimates a causal effect, but the effect size is
assumed to be linear rather than having adstock and diminishing returns.