Question 1

What LinkedIn Ads data can I load into BigQuery?

Accepted Answer

On the structural side, accounts, campaign groups, campaigns and creatives. On the reporting side, analytics pivoted by campaign, creative, company and other dimensions, with metrics covering impressions, clicks, spend, conversions and the video and engagement measures LinkedIn exposes. Take the structural tables even if you only care about numbers, because analytics rows reference identifiers that mean nothing until you join them to campaign and creative names.

Question 2

How does LinkedIn Ads incremental sync work?

Accepted Answer

Analytics are fetched in daily windows and checkpointed by date, then recent days are read again on a configurable lookback and merged over the existing rows. The default is two days, and member company reporting forces a minimum of four because those numbers settle more slowly. Restatements outside the window will not refresh on their own, so widen it if your attribution runs longer. Resource entities like campaigns and creatives are read in full each run.

Question 3

Can I match LinkedIn Ads data to target accounts in BigQuery?

Accepted Answer

That is the main reason to do this. Member company reporting gives you a company dimension on the analytics, which you can join to your own account list once both are in the same place. Pivots by industry, seniority, job function and company size are available too. LinkedIn applies privacy thresholds of its own, so small segments may be suppressed at source and will simply be absent from the data rather than showing as zero.

Question 4

Should I partition LinkedIn Ads tables in BigQuery?

Accepted Answer

For the analytics tables, yes, using the report date. Analytics grow one row per entity per day per pivot, so they get large faster than people expect and partitioning is what keeps a filtered query from reading everything. The structural tables are small and do not need it. Set the partition key when you create the sync, because BigQuery cannot repartition in place and changing it afterwards means rebuilding the table.

Question 5

Why load LinkedIn Ads into BigQuery?

Accepted Answer

Because business to business advertising is judged on pipeline rather than clicks, and pipeline lives in the CRM. Connecting campaign spend to opportunities needs both datasets in one place, and the account level pivots only pay off when you can match them to your own account list. Scheduled ETL keeps both the campaign structure and the daily numbers current, so the history stays put instead of adjusting under you.

Sync LinkedIn Ads to BigQuery

Sync LinkedIn Ads to BigQuery with Polytomic

Company level reporting

Daily analytics windows

Backfill from a start date

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