Google Ads Integration: Connect Google Ads to Analytify (2026)
Bring Google Ads data into a governed analytics warehouse with Analytify.
Why Connect Google Ads to Analytify
Google Ads UI shows you Google-attributed conversions. To compare Google to other channels honestly and compute true LTV-aware ROAS, you need Google Ads data in your warehouse alongside everything else.
Bringing Google Ads data into Analytify lets you:
- Cross-channel ROAS using your conversion definition (Google vs Meta vs TikTok vs organic).
- Campaign LTV — does Search bring higher-LTV customers than Display? Performance Max?
- Keyword-level revenue attribution joined to your warehouse conversion data.
- Multi-touch attribution models (Markov, Shapley, position-based) on full data.
- Spend pacing dashboards with alerts on overspend / underdelivery.
What Data the Integration Syncs
The connector syncs Google Ads objects via the Google Ads API:
| Object | Key fields | Use case |
|---|---|---|
| Campaigns / Ad Groups / Ads | name, type (Search/Display/PMax/Shopping), budget | Spend analysis |
| Keywords / Search Terms | keyword, match type, search query | Search query analysis |
| Daily Performance Reports | spend, impressions, clicks, CTR, CPC, conversions | Performance reporting |
| Conversions | conversion_action, value, attribution model | Attribution analysis |
| Audiences / Remarketing Lists | list memberships | Audience effectiveness |
| Asset Performance | PMax assets, responsive search ads, video | Creative analytics |
How to Connect Google Ads Data to Analytify
Because Analytify doesn’t ship a native Google Ads connector, the pattern is: Google Ads → ELT tool → cloud warehouse → Analytify. Here’s how it works:
- Set up an ELT pipeline from Google Ads to your cloud warehouse. Most teams use Fivetran, Airbyte, or Stitch — all three offer pre-built Google Ads connectors and land the data in Snowflake, Postgres, BigQuery, or Databricks on a schedule (typically hourly).
- Connect Analytify to the destination warehouse using the native connectors (PostgreSQL, Snowflake, MySQL, Microsoft SQL Server, MongoDB). The Analytify Postgres or Snowflake integration walks through this setup.
- Build dbt staging models on the raw Google Ads tables to flatten properties, normalise types, and define consistent dimension and measure logic.
- Define the semantic layer in Analytify on top of your dbt models — measures and dimensions over the Google Ads data, joinable with your other warehouse data.
- Verify counts against Google Ads’s native reporting for the past 30 days before going live.
Native connector roadmap. A native Google Ads connector is on the Analytify roadmap. Talk to us if going native vs warehouse-routed matters for your evaluation timeline.
Sample Dashboards You Can Build
- Channel ROAS Comparison — Google vs Meta vs TikTok vs organic, same conversion definition, real revenue.
- Campaign LTV by Match Type — exact match vs broad match cohorts; which brings higher-LTV customers?
- Search Term Mining — actual search queries triggering ads, with revenue attribution; identify negative keyword opportunities.
- Multi-Touch Attribution — Markov / Shapley models on Google Ads + GA4 + CRM full path data.
- Performance Max Asset Performance — drill into PMax black-box reporting using API-level asset data.
- Daily Spend Pacing — actual vs planned spend with alerts; identify campaigns burning budget on low-ROAS terms.
How the Integration Works (Architecture)
The Analytify Google Ads connector uses the Google Ads API (v15+) to pull performance reports, search-term data, and conversion data into your warehouse. Daily and hourly grain depending on your use case.
Data lands in `raw.google_ads.*` tables. dbt staging models normalise across MCC accounts and currencies. The semantic layer joins Google Ads spend with GA4 / Stripe / Shopify conversion data for unified attribution.
Troubleshooting Common Issues
- Google-reported conversions vs warehouse conversions. Google uses its own attribution model. Calculate both, compare, pick the one that matches your business reality.
- Performance Max black-box. PMax asset-level reporting is API-only. Analytify surfaces it; the UI doesn’t.
- Smart Bidding training data. Send your warehouse-computed conversion values back to Google via offline conversion upload for better Smart Bidding (especially for businesses with long sales cycles).
- API quota. Google Ads API has daily operation limits; the connector batches efficiently.
Pricing and API Limits
Google Ads API access is free for Google Ads advertisers. Daily operation limits scale with account size. The Analytify connector adds zero direct cost from Google; only warehouse compute + Analytify per-user pricing.
Ready to ship governed Google Ads analytics?
FAQs
Why not use the Google Ads UI?
The UI is good for tactical management but limited for cross-channel attribution and LTV analysis. To compare Google honestly with other channels, you need warehouse-level analytics.
Does it integrate with GA4?
Yes — combine Google Ads + GA4 + Stripe / Shopify data in the warehouse for full-path attribution.
Can I send conversion uploads back to Google?
Yes via Google Ads offline conversion uploads, automated through reverse ETL (Hightouch, Census). Feeds Smart Bidding with first-party conversion data.
What about Google’s data-driven attribution?
Google’s DDA is a black box. Analytify lets you implement transparent attribution models (Markov, Shapley) on full data so you can validate or replace Google’s.
Performance Max — is it worth analysing?
Yes. PMax UI is intentionally opaque, but API-level data exposes asset performance, audience signals, and channel breakdown. Analytify surfaces it.
Multi-account / MCC support?
Yes — connect MCC accounts; child accounts sync underneath. Reporting can be unified or split.
Real-time spend monitoring?
Hourly cadence is supported. For sub-hour alerts, use Google Ads scripts to push events into your warehouse.