A spend table maintains all of the details about your company's marketing spend, campaign details, and utm parameters.
Every marketing dollar cannot be associated to a specific customer, so the spend data is aggregated before it is joined to the data in the activity stream. This ensures that all marketing spend details are used when evaluating marketing performance.
The spend table can be joined to any grouped dataset to enrich it with marketing details like total spend, clicks, impressions, etc. The data in the spend table is aggregated before it is joined to the grouped dataset.
See How To: Add Spend Data to your Dataset to see how it's used.
A spend transformation generates a dataset with six required columns and any number of additional columns related to marketing (campaign, utm parameters, etc). Spend transformations are a specific implementation of an enrichment transformation.
|Unique id for each record in the table|
|Timestamp in UTC of the data date|
|Name of the ad source generating the data (ie. 'adwords'). The ad source values should match the ad source feature in your started session activity.|
|Number of impressions|
|Number of clicks|
|additional_columns||These columns are the additional features related to the marketing spend (ie. campaign, creative, etc).|
The names should be descriptive of the data they represent (as opposed to the generic feature_1, feature_2, feature_3 naming used for activities).
How To: Create a Spend Table
Watch this step-by-step tutorial to learn how to add a spend table.
Explore the transformation library for examples by data source.
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Updated over 1 year ago