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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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Google BigQuery.
Hightouch sync.
Google Ads.

Trusted by data teams at

Integrate your data in 3 easy steps

  1. 01

    Add your source and destination

    Connect to 15+ data sources, like Google BigQuery, and 140+ destinations, like Google Ads.

    Google BigQuery.

    Connect

    Connector beam.
    Google Ads.

    Log in

  2. 02

    Define your model

    Use SQL or select an existing dbt or Looker model.

    Define your model
  3. 03

    Sync your data

    Define how fields from your model map to Google Ads, and start syncing.

    Google BigQuery.
    Connector beam.
    Google Ads.
    Google BigQuery.

    email

    Connector beam.
    Google Ads.

    email

    Google BigQuery.

    name

    Connector beam.
    Google Ads.

    name

    Google BigQuery.

    total_orders

    Connector beam.
    Google Ads.

    all_orders

    Google BigQuery.

    last_login

    Connector beam.
    Google Ads.

    last_login

Model your Google BigQuery data using any of these methods

  • dbt Model Selector

    Semi-opaque, open dropdown with three example dbt model names such as 'dbt.model.name.1'.

    Sync directly with your dbt models saved in a git.

  • Looker

    Semi-opaque open dropdown with three example Look names such as 'look.1'.

    Query using looks. Hightouch turns your look into SQL and will pull from your source.

  • SQL Editor

    Empty SQL editor.

    Create and Edit SQL from your browser. Hightouch supports SQL native to Google BigQuery.

  • Table Selector

    Semi-opaque open dropdown with three example table names such as 'schema.table.name.1'.

    Select available tables and sheets from Google BigQuery and sync using existing views without having to write SQL.

  • Customer Studio

    Visual query builder reading 'All rows that...' with a button labeled 'select a property'.

    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.

    Hightouch docsGoogle Ads APIGoogle Ads support
  • 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

Data Source.png

Step 2: Connect Hightouch to your destination

Destination.png

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.

Data Model.png
Audiences.jpg

Step 4: Choose your Primary Key

With Hightouch you can map on any unique key, not just users and accounts.

primary key.png

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.

column mappings.png

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.

Sync.png

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.

Google BigQuery.

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 BigQuery
Google Ads.

About 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 Ads

Other Google BigQuery Integrations

Google BigQuery to Zoho CRM

Hightouch Playbooks: Best practices to leverage reverse ETL

  • Manage Omnichannel Split Testing across Facebook and SFMC.

    Manage Omnichannel Split Testing across Facebook and SFMC

    This playbook will show you how to manage multivariate testing across Facebook and Salesforce Marketing Cloud with Hightouch Audiences.

  • Create Facebook Lookalike Audiences from High Value Users.

    Create Facebook Lookalike Audiences from High Value Users

    In this playbook, you will learn how to use Hightouch to sync an audience of high-value customers to Facebook to generate lookalike audiences.

  • Sync Nested Object Data to Braze.

    Sync Nested Object Data to Braze

    This playbook will help you understand how and why you should be leveraging nested object arrays in Braze to deeply personalize your omnichannel marketing campaigns.

Read more about Google Ads

  • The Marketers Guide to TikTok Conversion Tracking.

    The Marketers Guide to TikTok Conversion Tracking

    Maximizing TikTok ROI through a best-in-class integration strategy

  • The Definitive Guide to Conversion APIs & Web Pixels.

    The Definitive Guide to Conversion APIs & Web Pixels

    Facebook, Google, Snapchat, and Tiktok are adopting conversion APIs to capture data that web pixels and cookies can no longer collect. Here's an overview on how to adopt these new server-side advertising APIs.

  • How to send data from Redshift to Google Ads.

    How to send data from Redshift to Google Ads

    Learn how you can move data from Redshift to Google Ads in 6 easy steps

  • How to send data from Snowflake to Google Ads.

    How to send data from Snowflake to Google Ads

    Learn how you can move data from Snowflake to Google Ads in 6 easy steps

Read more about Hightouch

  • What is Reverse ETL? The Definitive Guide .

    What is Reverse ETL? The Definitive Guide

    Learn everything there is to know about Reverse ETL, how it fits into the modern data stack, and why it's different than ETL.

  • What is Operational Analytics & Why You Should Use It.

    What is Operational Analytics & Why You Should Use It

    Operational Analytics shifts the focus from simply understanding data to taking action on it in the tools that run business processes. Instead of using dashboards to make decisions, Operational Analytics is focused on turning insights into action – automatically.

  • dbt Cloud: 4 Reasons for Data Teams to Embrace it.

    dbt Cloud: 4 Reasons for Data Teams to Embrace it

    The biggest benefit that dbt Cloud offers to data teams and analytics engineers? Freedom from distractions, and the ability to focus where you can add unique value making sense of your company's data.

Activate data to any of your marketing and advertising tools

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Austin Hay.
Ramp logo.

This might be one of the greatest inventions for technical marketers since the advent of legacy CDPs back in 2015.

Austin Hay

Austin Hay

Head of Marketing Technology

Ramp

See story

Your data is always secure

Unlike other SaaS tools, Hightouch never stores any of your data.
SOC 2 Type 2 compliant.

SOC 2 Type 2 compliant

Your data stays secure, available, and confidential. To see our report, .

GDPR compliant.

GDPR compliant

If you’re in the EU, your data is only processed on EU data centers.

HIPAA compliant.

HIPAA compliant

Healthcare companies like ThirtyMadison, Chapter Health, and Headway trust Hightouch.

CCPA compliant.

CCPA compliant

To see our DPA (Data Processing Addendum), .

52%

increase in return on ad spend

20%

improvement in email engagement

60%

lift in customer acquisition

It takes less than 5 minutes to activate your data. Get started today.

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