LinkedIn Ads Advanced Settings

For users who have some experience working with their data in Panoply, there are a number of items that can be customized for this data source.

1. Pivot Grouping: For Ad Analytics only, the Pivot group defines the way the data is grouped. Default is Campaign. For a list of available pivots, see the Data Dictionary.
2. Incremental Load: Panoply will collect all of your Ad Analytics data on the initial load. Panoply will use the last successful collection date minus 30 days as the start date on subsequent collections.
3. Post-Click Attribution: This conversion window is the timeframe within which conversions will be attributed to a LinkedIn ad. LinkedIn advertisers can customize their conversion windows based on their reporting preferences. LinkedIn records conversions within the selected conversion window of a member clicking on a LinkedIn Ad.
4. View-Through Attribution: This conversion window is the timeframe within which conversions will be attributed to a LinkedIn ad. LinkedIn advertisers can customize their conversion windows based on their reporting preferences. LinkedIn records conversions within the selected conversion window of a member seeing a LinkedIn Ad.
5. Destination Schema: This is the name of the target schema to save the data. The default schema for data warehouses built on Google BigQuery is panoply. The default schema for data warehouses built on Amazon Redshift is public. This cannot be changed once a source has been collected.
6. Destination Prefix: The Destination Prefix determines the name of the tables created. Panoply automatically uses linkedin_ads as the destination prefix, then adds the name of the resource, such as linkedin_ads_endpoint. To change the prefix for these tables, enter your preferred prefix. If you enter my_linkedin for example, the resulting table would be my_linkedin_endpoint such as my_linkedin_insights.
   • The Ad Analytics endpoint combines the prefix with the name of the pivot to create the table name, with the syntax linkedin_ads_ad_analytics_<pivot> where pivot is a dynamic field representing the name of the collected pivot.
   • All other endpoints combine the prefix with the name of the endpoint to create the table name, with the syntax linkedin_ads_<endpoint> where endpoint is a dynamic field representing the name of the collected endpoint.
7. Exclude: The Exclude option allows you to exclude certain data, such as names, addresses, or other personally identifiable information. Enter the column names of the data to exclude.
8. Truncate: Truncate deletes all the current data stored in the destination tables, but not the tables themselves. Afterwards Panoply will recollect all the available data for this data source.
9. Click Save Changes and then click Collect.
   • The data source appears grayed out while the collection runs.
   • You may add additional data sources while this collection runs.
   • You can monitor this collection from the Jobs page or the Data Sources page.
   • After a successful collection, navigate to the Tables page to review the data results.