Sync data from
Google BigQuery to Google Ads
Connect your data from Google BigQuery to Google Ads with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
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Integrate your data in 3 easy steps
01
Add your source and destination
Connect to 15+ data sources, like Google BigQuery, and 140+ destinations, like Google Ads.
Connect
Log in
02
Define your model
Use SQL or select an existing dbt or Looker model.
03
Sync your data
Define how fields from your model map to Google Ads, and start syncing.
email
email
name
name
total_orders
all_orders
last_login
last_login
Model your Google BigQuery data using any of these methods
dbt Model Selector
Sync directly with your dbt models saved in a git.
Looker
Query using looks. Hightouch turns your look into SQL and will pull from your source.
SQL Editor
Create and Edit SQL from your browser. Hightouch supports SQL native to Google BigQuery.
Table Selector
Select available tables and sheets from Google BigQuery and sync using existing views without having to write SQL.
Customer Studio
For less technical users, pass traits and audiences from Google BigQuery using our visual segmentation builder.
Where can you sync your Google BigQuery data in Google Ads
Customer Match (User) Lists
Customer Match lets you target ads to your customers using the data they share with you. You can upload it into Google Ads to incorporate this targeting into your campaigns.
Call Conversions
Phone call conversion tracking helps you track when your ads lead to different kinds of phone calls. By importing call conversion information into Google Ads, you can track when phone calls lead to sales or other valuable customer actions. To learn how to import phone call conversions, refer to Import phone call conversions.
Enhanced Conversions
Using the Google Ads API, you can leverage enhanced conversions by sending first-party customer data in the form of conversion adjustments. Google uses this additional data to improve the reporting of your online conversions driven by ad interactions.
Offline (Store) Conversions
Sometimes, an ad doesn't lead directly to an online sale, but instead starts a customer down a path that ultimately leads to a sale in the offline world, such as at your office or over the phone. By importing offline conversions, you can measure what happens in the offline world after your ad results in a click or call to your business.
Conversion Adjustments
A customer's typical conversion path ends after they convert, but this isn't always the case. Customers return retail purchases, cancel reservations, or perform actions that increase their value to your business. To account for these changes in conversion value, you can adjust the value of a conversion after it's reported in Google Ads.
Does this integration support in-warehouse planning?
Yes, if you integerate Google BigQuery and Google Ads using Hightouch, in-warehouse planning is supported.
Great, but what is in-warehouse planning?
Between every sync, Hightouch notices any and all changes in your data model. This allows you to only send updated results to your destination (in this case Google Ads). With the baseline setup, Hightouch picks out only the rows that need to be synced by querying every row in your data model before diffing using Hightouch’s infrastructure.
The issue here is this can be slow for large models.
Warehouse Planning allows Hightouch to do this diff directly in your warehouse. Read more on how this works here.
Why is it valuable to sync Google BigQuery data to Google Ads?
Thanks to Google BigQuery, it's easier than ever to access your customer data, run complex queries, and segment your customers/users into various categories or audiences.
However, to target users and send conversion data to Google Ads, you need access to all of the unique behavioral data (e.g., last-login date, items in cart, pages viewed, etc.) and core business metrics like lifetime value, workspaces, subscriptions, annual recurring revenue, etc. that lives in Google BigQuery. You then need to be able to package this data to send to Google Ads in the format expected. Plus, you want to do this in a real-time and automated way.
Google Ads is only as good as the data you give them, and if you truly want to optimize your ad spend, increase your match rates, and drive conversions, you need to provide custom data from Google BigQuery within your warehouse.
Maybe you want to retarget users who abandoned their shopping cart in the last seven days, or upload a list of high-value customers to identify potential lookalike audiences, or perhaps you want to upload offline conversion events to reduce your customer acquisition costs and increase your return on ad spend.
Why should you use reverse ETL to connect Google BigQuery and Google Ads data?
In the past, uploading customer data to Google Ads meant hopping back and forth between your various SaaS applications or asking your data team for CSV files. Neither of these options is preferable because marketing teams want to self-serve, and data teams don't enjoy constantly fulfilling one-off marketing requests.
Even worse, if you truly want to optimize your advertising campaigns, you need to be uploading fresh data consistently. Non-fresh data can be expensive for ads. If you're using CSVs to define who to exclude from paid ads and you're uploading that data weekly, that's potentially one week of irrelevant ads. As a workaround, engineering teams will integrate directly with the Google Ads API, and build and maintain custom in-house pipelines. The problem is that a single API change can break everything, and data engineers don't want to spend their time building and maintaining pipelines.
With Hightouch, you can leverage the existing data models and customer segments your engineering team has defined in your warehouse and sync that data directly to your ad platforms in real time. You can schedule your data syncs to run automatically, on a set cadence, or even for the exact duration of your marketing campaign. Hightouch lets your data teams establish the guardrails for your marketers to self-serve and build custom audiences through a drag-and-drop interface.
How to integrate BigQuery and Google Ads
There are 5 common options to activate your BigQuery data and sync it with Google Ads. We've outlined and reviewed the pros and cons of each option below:
Option 1: Reverse ETL (Hightouch)
Reverse ETL leverages your own technology stack and runs on top of the warehouse. This means that you own all of your data and you don’t have to worry about another 3rd party vendor. Using tools like Hightouch you can define your data in BigQuery with your own custom data models or SQL.
After this, all you have to do is map the appropriate columns from Google BigQuery to Google Ads and start your data sync. You can schedule this manually or run your syncs on a set interval. All of this can be done in six simple steps
Step 1: Connect to Your Data Source

Step 2: Connect Hightouch to your destination

Step 3: Create a data model or leverage an existing one
You can use SQL to define your data directly in the Hightouch UI or even leverage your existing data models.


Step 4: Choose your Primary Key
With Hightouch you can map on any unique key, not just users and accounts.

Step 5: Create Your Sync
Once you have created your sync, you simply have to choose the appropriate columns and map them to the fields in your end destination.

Step 6: Schedule Your sync
Once your sync is created you can run it manually, define a set interval, or schedule it to run after a dbt job is completed.

The first integration with Hightouch is completely free so you can actually get started today. If you want to learn more about Reverse ETL, download our guide.
Option 2: Manual CSV Files
A relatively simple way to move data to Google Ads involves downloading CSV files from BigQuery and uploading them directly to Google Ads. The problem with this method is that data goes stale really quickly when it is stored in a CSV file.
This way of moving data is also extremely manual. Your data engineers are forced to download/upload a different data set every time your marketing team wants to run a new campaign or target a different audience. Doing this pulls away from the actual job they could be fulfilling.
Option 3: Custom Integrations
Building your own in-house data pipeline is one way to tackle the challenge of moving data from Google BigQuery to Google Ads. This can be extremely challenging though because custom data pipelines are time-consuming to build and difficult to maintain. The APIs for Google BigQuery and Google Ads are constantly changing, which means you have to constantly update your data pipeline upstream or downstream depending on where this update takes place.
Depending on your scale, it's possible that a custom data pipeline could suit your needs, but if you are planning on sending data to additional Ad platforms in the future, this is not scalable. Your engineers will be building and maintaining data pipelines instead of doing the jobs they were actually hired for.
Option 4: iPaaS (Integration Platform as a Service)
Integration platforms are relatively simple to implement because they let you build intuitive workflows to push data from one system to another. In general, these tools are mainly used for automating different tasks and processes to improve efficiency. They move your data from point “A” to “B” or the inverse of that. The problem is, these workflows create an illusion of accessibility.
At their core, they perform an event when a trigger is met and if you try to do anything remotely complex or unique to address your business needs, you will find that these workflows soon become extremely difficult to manage, with multiple if/then statements and various dependencies in every step. In many cases, you will even need to write some custom code to get them to work in the way that you want.
Option 5: CDPs (Customer Data Platform)
With a CDP you can easily consolidate all of your customer data into a central platform where it can then be sent directly to Google Ads. This is slightly problematic though because it creates a second source of truth in addition to your data warehouse.
Using a CDP requires you to move data out of your own cloud ecosystem and into a 3rd party vendor and this can create challenges from a compliance standpoint. Although CDPs are a great solution for marketers, they can be quite challenging to implement, with the average time taking anywhere from six months to an entire year. From a time-to-value standpoint, this is not very effective.
Upload lists to Google Ads to run ads based on certain attributes within your database, such as people who have visited your site
Run lookalike audiences on Google Ads using subsets of your users rather than all of them
Continuously fuel your Google Ads custom audiences with live data so that data never goes stale
Why sync data from
Google BigQuery to Google Ads?
Every company advertises through Google Ads regardless of size or industry. Advertising is expensive though, and figuring out how to increase ROAS, lower CAC, and improve campaign performance is extremely important. All of your unique customer data (e.g. product usage data, key events, custom audiences, etc.) lives in BigQuery. However, custom integrations are prone to failure and manual CSV files become stale really fast. To truly take your marketing campaigns to the next level, your marketing team needs to be able to self-serve and sync custom audiences to Google Ads at a moment’s notice.
About Google BigQuery
BigQuery is a fully-managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence.
Learn more about Google BigQueryAbout Google Ads
Google Ads remarketing allows you to advertise to people who have previously visited your website, used your mobile app, or who are in your CRM databases, by showing them relevant ads when they visit other sites or search on Google. The user lists used in remarketing can also be used for other types of audience targeting, such as Customer Match.
Learn more about Google AdsOther Google BigQuery Integrations
Other Google Ads integrations
Hightouch Playbooks: Best practices to leverage reverse ETL
Read more about Google Ads
Read more about Hightouch
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52%
increase in return on ad spend
20%
improvement in email engagement
60%
lift in customer acquisition