Source Advertising

Google Ads Data, Delivered to Your Database

Q: Why don’t Google Ads and GA4 agree?

They use different attribution models, count conversions on different dates (Google Ads on the click date, GA4 on the event date), and see different subsets of the journey. Both are right by their own definition. The marts carry each source’s numbers in clearly labelled columns instead of pretending one of them is ‘the truth’.

Q: Can you pull our whole MCC?

Yes. Accounts under a manager account are synced into one schema with account, currency and time zone carried on every row and normalized where you need totals. Cross-account reporting stops being a spreadsheet exercise.

Q: How far back can you backfill?

On the API path, first sync loads the report history the API exposes for your account. On the native transfer path, BigQuery Data Transfer Service runs scheduled backfills that load history in chunks of up to 180 days, as far back as your account’s history reaches. Either way, the practical bound is your account’s history, not the pipeline — and from first sync onward everything accumulates in your database under your retention.

Q: Do you ever write to our Ads account?

No. Access is read-only reporting: no campaign changes, no budget edits, no bidding. The pipeline reads reports and nothing else.

Cleaned, enriched and modeled into ready-to-query mart tables. Currently on BigQuery. We run the pipeline, you own the data.

Insightlytics ingests your Google Ads data — spend, clicks, impressions and conversions from campaign down to the search term — through the native BigQuery Data Transfer, the Google Ads API, or both. We normalize micros into money, re-sync recent days until late conversions settle, and model it into mart tables that sit next to your GA4 sessions — so cost per acquisition is computed from spend and outcomes in one place. You get the tables and the history; we run the transfers, the API pacing and the monitoring. Everything is written to a database you own — BigQuery today, with more warehouses on the roadmap.

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The problems we solve

Google Ads data is easy to look at and hard to report on — the numbers keep moving, the formats fight you, and the join to your analytics is on you. These are the problems this pipeline is built around.

How do you join Google Ads cost with GA4 sessions?

Out of the box, you don’t — spend lives in one silo and sessions in another, and the join is on you. It needs campaign keys that actually match, UTM conventions that survive renames, and both sides landed in the same database at the same grain. Most teams settle for eyeballing two dashboards instead.

We build that join into the marts: campaign names parsed into market, product and funnel-stage columns, keys aligned with your GA4 session marts, and cost sitting next to the sessions and revenue it bought. That’s the foundation a real cost-per-acquisition number stands on.

Which CPA is the true CPA — Google Ads or GA4?

They will not agree, ever. Different attribution models, different count dates (Google Ads writes conversions back to the click date, GA4 counts events on the day they happen), different visibility into the journey. Neither is broken, and picking one at random just moves the argument to next quarter.

The marts put both next to each other in clearly labelled columns, computed over the same spend rows — which turns a recurring argument into a known, stable difference you can decide against.

Why do last week’s Google Ads conversions keep changing?

Conversion lag. A click today can convert next week, and Google writes that conversion back onto today’s row. Any export that pulls each day once diverges from the interface within days.

The pipeline’s rolling re-sync window exists precisely for this — recent days are re-pulled until they stop moving, so the marts converge on final numbers instead of freezing the early ones.

Why is the native Google Ads transfer so hard to query?

The BigQuery Data Transfer lands dozens of fixed report tables — campaign, ad group, ad, a family of *Stats tables — with cost serialized as micros: millionths of the account currency, as ten-digit integers. Forget the division once and a dashboard reports a million times the spend.

The transfer is a delivery mechanism, not a model. We treat it as one: normalization, rounding policy and currency conversion happen in the pipeline, and what your team queries are marts where cost is simply money.

Why don’t search terms sum to campaign totals?

Two reasons. Google withholds low-volume search terms for privacy, so the search-term report is a large sample, not a census. And the terms aren’t in the native transfer at all — they exist only on the API route, which is why transfer-only setups never see them.

We pull search terms through the API alongside whichever route carries the rest, and the marts record both levels so the withheld share is a measurable number, not a suspicion.

How we work with Google Ads data

Two routes exist — the native BigQuery Data Transfer and the Google Ads API — and choosing between them is a consulting decision, not a checkbox.

The native transfer lands Google’s fixed report tables on schedule, re-lands a 7-to-30-day window as conversions settle, and isn’t metered by your developer token — but it doesn’t carry search terms and its schemas are take-it-or-leave-it. The API can express any report GAQL can — search terms included — but it’s paced by access tiers and operation quotas, and metric–segment compatibility rules decide what can be pulled together. Which route fits depends on the reports you need, the size of your MCC and how deep the join to GA4 has to go.

We settle that together at the architecture review. Many accounts end up on both — the transfer for the standard report tables, the API for search terms and custom shapes — feeding the same marts. Either way, running the pipeline is our job: no connector to configure, no quota to babysit.

Refresh, backfill and monitoring

Refresh

Daily. API syncs re-pull a rolling lookback; the native transfer’s refresh window re-lands recent days — either way, late conversions and invalid-traffic removals flow through instead of freezing.

Backfill

First sync loads the report history the API exposes; the native transfer backfills in chunks of up to 180 days, as far as your account’s history reaches. From then on everything accumulates under your retention, not the platform’s.

Monitoring

Every sync is checked for freshness, volume and schema drift. Google Ads API versions rotate on a fixed cadence; upgrades happen inside the pipeline, so field renames never reach your marts.

Delivered to a database you own

Every table — raw and mart — is written to your own database, under your billing and your access controls. Our access is read-only, and if we part ways, everything stays with you.

BigQuery Marts are built in your own BigQuery project — our access is read-only, and the tables are always yours.

BigQuery is the supported database today. If you run a different warehouse, tell us — the roadmap is driven by requests.

What lands in your database

Not dozens of transfer tables with micros in them — named mart tables at documented grain, refreshed daily and ready to query.

mart_ads_spend_daily Spend, clicks and conversions by campaign per day — one schema across Google and Meta.
mart_revenue_by_channel Ad cost next to the revenue it drove, by channel and day.

The standard marts are the starting point, not the ceiling. When your questions need a different shape — another grain, another split, a blended-spend union with your Meta Ads marts — we define additional marts for your stack as part of the engagement.

What you can run on Google Ads data

Every capability we ship works on these marts from day one.

Frequently asked questions

If something is still unclear, a discovery call clears it up fast.

Why do recent conversions keep changing?

Because Google attributes conversions back to the click date, and conversions keep arriving for days or weeks after the click. A report pulled on Monday about last week is not wrong — it’s early. The pipeline re-syncs a rolling window of recent days until the numbers settle, so your marts converge on the final figures instead of freezing the early ones.

Why don’t Google Ads and GA4 agree?

Can you pull our whole MCC?

How far back can you backfill?

Do you ever write to our Ads account?

Spend and outcomes in one database — and a CPA you can defend.