In addition to paid media there may be other marketing actions taken place that affect the KPI of interest.
Organic media variables
Organic media variables are media activities that have no direct cost. These can include, but are not limited to, impressions from newsletters, a blog post, social media activity, or email campaigns. Organic media variables have the option of being modeled with reach and frequency, and have Adstock and Hill effects, just like paid media variables. The only difference between organic and paid media is that organic media does not have an associated cost. So, ROI priors cannot be used with organic media and results pertaining to ROI, such as response curves and budget optimization, are not available for organic media variables.
Causal effects and % (percent) contributions are given for organic media variables and are calculated in the same manner as paid media, as described in Incremental outcome definition. As with paid media, the incremental outcome of an organic media channel is defined as the expected difference in the outcome under the observed channel's media execution compared with the counterfactual scenario of not running that channel.
Non-media treatment variables
Non-media treatment variables are marketing activities that are 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. They have no direct marketing cost associated with them but, unlike organic media variables, they are not media related and there are no Adstock and Hill effects. They differ from control variables because they are considered to be intervenable and therefore are treatment variables under the causal model. As such, incremental outcome and % (percent) contributions are provided for non-media variables.
Similar to paid and organic media, the incremental outcome of a non-media
variable is defined as the difference in expected outcome between two
counterfactual scenarios. The first scenario is setting the non-media variable
to the observed historical value for each geo and time period. The second
scenario is setting the non-media variable to either the minimum of the
non-media variable (default), the maximum or a user supplied value (see
non_media_baseline_values argument in ModelSpec). The incremental outcome
is the expected outcome under the first scenario subtracted by the expected
outcome under the counterfactual scenario.
The reason the second counterfactual is not setting the non-media variable to zero, as is the case with paid and organic media, is that zero is often not an appropriate counterfactual for non-media variables. For example, if the non-media variable is price it may make sense to think of the causal effect of setting the price to its observed value compared with the minimum price the product sold for but it does not make sense to set the price to zero.
Deciding if a variable is a non-media treatment variable or a control
The main difference between a non-media variable and a control is that non-media variables are considered to be intervenable, and therefore are treatments under the assumed causal model. Controls, otherwise known as confounding variables, are not intervenable and are assumed to affect both the treatment variables and the outcome. For more information, see Causal Graph.
As an advertiser, if you can intervene and change a variable's value (such as changing the price or running a promotion), the variable is more likely a non-media variable than a control. If the variable is outside the control of the advertiser, such as, broad economic indicators or geo or national level demographics then it is most likely a control.
Deciding if a variable is an organic media variable or a non-media treatment variable
Organic media variables behave like paid media variables with no associated cost. They usually are impression or reach and frequency based, and are typically advertising activities with no direct cost, such as social media posts and email campaigns. Non-media variables also have no direct cost but are not media related. Typically, non-media variables are related to changes in the underlying product, such as the price, a promotion or a change in the product's packaging. Another way to determine what is appropriate for your variable is that organic media have Adstock and Hill effects applied, but non-media variables don't.
Differences between the types of input variables
The following table can help determine what input variable is appropriate:
| Input Variable | Cost | Adstock/Hill | Intervenable | Effect (% Contribution) |
|---|---|---|---|---|
media |
x | x | x | x |
non_media |
- | - | x | x |
organic_media |
- | x | x | x |
controls |
- | - | - | - |
For more information about assumed causal relationships between input variables in Meridian, see Causal Graph.