⏏️ Interpreting the Outputs

One of the advantages of Recast over other attribution methodologies is it takes into consideration marketing spend across all your channels. Since customer journeys may be influenced by multiple marketing efforts, having a holistic overview of all spend is essential in order to measure true incrementality of marketing spend. In order to do this Recast needs a comprehensive and complete view of your marketing program.


Recast cannot measure a channel’s incrementality in isolation from other channels. Therefore, we need data for all sources of marketing spend. If you are spending money in a channel and Recast builds a model without accounting for that channel, the amount of revenue being driven by that channel is going to be credited to other channels or the organic component of the model. This results in upward bias where we overstate the effectiveness of some other component of the model.

Short term experiments are okay to not include, but once a channel becomes a significant portion of your overall spend (e.g. 5%) and is going to be a long-term channel, we need to account for it in the model. See this page on the steps to add a channel to your Recast model.


Another problem that can introduce bias is if the data Recast receives is not complete. By complete, we mean that if a day appears in your dataset all marketing channels and revenue/conversion numbers must be accurate for that day. A variety of causes can cause data on a given day to be incomplete:

  • The reporting for a given channel is behind, so we don’t have any data for the last week in a channel even though all the other channels have data.
  • Spend (or revenue) numbers “trickle in” over time so we might have some spend recorded for days in the last week, but those numbers will be revised upward as time goes on.

Any incomplete data will cause bias in the model, although the severity depends on the magnitude of the difference between the reported numbers and the actual numbers.