Exogenous Variables

Brands often raise the question “should we control for x?” where x is some exogenous variable that impacts their sales. Things like:

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Since accurate inferences don’t require explicitly controlling for these variables we generally recommend against controlling for them in the interest of model parsimony.

Contextual Variables

There are some cases where it might make sense to include these exogenous variables in the model. Particularly, we think it makes the most sense to include these variables when we have the ability to predict the value of these variables into the future. So things like “the weather” often don’t make sense to include, but if your business is highly dependent on a variable like “new housing starts” and you feel your organization has the capacity to predict this variable into the future, then it might make sense to include that variable in the model.

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One good use case for contextual variable is for something like a price or offer change. For example, if you increase the price of your product from $5 to $7 then we can include the price change as a contextual variable in order to be able to estimate how that price increase impacted both your base sales (through the intercept) as well as your marketing performance (via the ROIs on your marketing effectiveness). Since this variable is controlled internally it’s very easy to forecast and will make our forward-looking forecasts and optimizations more accurate (since the change in performance due to the price change is important contextual information).

Non-Paid Media Variables

There are other types of marketing activity that don’t exactly look like typical paid marketing activity. These types of activity might include:

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Otherwise, if you have variables that are part of your marketing mix and check the boxes above, then it definitely could make sense to include them in the model. Site visits by sales people and handing out samples for pharmaceutical products fall into this bucket and are good candidates for inclusion in the model.

Promotions

We generally handle promotions in our model using spikes.

See our help page Practical Considerations for Placing Spikes for advice on this.

Modeling Channels without Vary Spend Over Time

Some clients invest in tactics like sponsoring a sports team or having their brand featured on a radio show for a prolonged period of time. In a similar vein, they may also pay a PR firm a fixed fee to generate them organic press. These types of channels represent unique challenges for Recast give we rely on variation in spend over time to understand the relationship between a channel and the client’s dependent variable.

Depending on the data available from clients on for these channels, we have identified 2 options for modeling them:

Option 1: Non-Spend Channel using Impressions

If impressions generated by the fixed spend are available by day or by week historically we can model it as a non-spend channel. The benefits of this are it captures the changing impressions over time, and then also allows the efficacy of the impressions to change over time, saturate, and have a time shift. The cons of this approach are that it can’t be represented in the optimizer, so will have to be factored in outside of this and some of the reports are not available for non-spend channels.

Option 2: Non-Spend Channel Using Binary Indicator Variable

If a variable impressions metric is not available historically, utilize binary indicator variables as Non-Spend channels to indicate when there are events that would drive impressions. This requires that the channel is not always-on, so there are some days we can observe with 0s.

Option 3: 😢

If historical impressions metric is not available, and the channel is truly always on, we won’t be able to estimate the efficacy of the fixed spend channel distinctly from the intercept.

Experiments

Details for how Recast incorporates information on Experiments has been moved to the Experiments page.