Question 1

Which entities and metrics are available?

Accepted Answer

On the resource side: accounts, account users, audiences, campaign groups, campaigns and creatives. On the analytics side: daily ad analytics pivoted by campaign, by creative, by member company, and by campaign crossed with member company. The metric surface covers impressions, clicks and spend, plus engagement, video, lead generation, and conversion metrics split into post click and post view.

Question 2

How does incremental reading work, and are restatements picked up?

Accepted Answer

Analytics are fetched in daily windows and checkpointed by date, then recent days are reread on a configurable lookback. The default is two days, and member company reporting forces a minimum of four because those figures settle more slowly. Restatements that land outside the lookback are not refreshed automatically, so if your attribution moves later than the window, widen it. Resource entities like campaigns and creatives are read in full each run, and both move together in one ETL run rather than as separate pipelines.

Question 3

Can we get demographic breakdowns?

Accepted Answer

Only in a specific sense, and it is worth being precise because the word means something broader in the LinkedIn interface. What is available here is member company reporting, optionally crossed with campaign, which is what account based measurement actually needs. It is not age, gender, seniority or job function. Some metrics are also unavailable on the member pivots, including approximate member reach and the card metrics, so a query that works on the campaign pivot will not always transfer.

Question 4

What happens when rows are removed?

Accepted Answer

Analytics rows are upserted at the date and pivot grain, and no tombstones are emitted, so a row that disappears outside a reread window is not detected as removed. Resource tables rely on full read reconciliation instead, which works because they are read in their entirety each run. In practice this matters less for advertising analytics than for transactional systems, because the grain is a date and a pivot rather than a mutable record.

Question 5

Why replicate LinkedIn Ads into a data warehouse?

Accepted Answer

Because business to business advertising is judged on pipeline, not on platform metrics, and pipeline lives in the CRM. ETL into a data warehouse lets you join campaign spend to opportunities and closed revenue at the account level, which is exactly what member company reporting is for. It also keeps a history at your grain rather than the platform default, so a change in how the interface reports last quarter does not quietly change your numbers.

Sync LinkedIn Ads to Snowflake

Sync LinkedIn Ads to Snowflake with Polytomic

Daily analytics windows

Campaign and company pivots

Backfill from a date cutoff

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