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The data you have on your customers is incredibly valuable. The only pesky problem is it is locked away in your Cloud Data Warehouse so your business teams can’t use it easily for marketing and sales campaigns.

Today we’re going to show you how you get customer data from BigQuery to Google Ads to target your Search, Youtube, Gmail, and Display ads on Google. And we’re going to do it in less time than it takes to make a cup of coffee.

But, first, let’s take a quick step back. Why send your data to Google Ads in the first place? You may have seen some of our reasoning from our posts on other channels, but we see several common use cases across clients where bringing the power of BigQuery to Google Ads can create real business value. Some of the common use cases we see include:

  1. Best Customer Lookalikes — Exporting lists of your best customers to Google Ads to find “similar” users to acquire.
  2. Churn Re-activation — Exporting lists of customers who have churned and you are trying to win back based on what they purchased in the past.
  3. Free to Paid Conversion — Exporting lists of free sign-ups you are trying to convert to paid customers.
  4. Upsell Non-Email Openers — The majority of your customers don’t read your emails, unfortunately. For those non-readers, you can get in front of them on Google and Youtube for cross-selling and upsell to new products. This is a clever use case we see all the time.

When activating your BigQuery customer data in Google there are a few key elements to consider:

  1. One time vs Ongoing Sync — Do you want the customer segment in BigQuery to be sent once to Google Ads? Or do you want it to update every day based on which customers now fit your segment criteria (ie. churned customers). Usually, we see customers want ongoing sync, but find it difficult to implement in practice.
  2. Measurement — Are you going to hold back any portion of your customer segment as a control group to measure if Google Ads is driving incremental revenue?

With GrowthLoop you can start syncing your BigQuery data to Google Ads in a few simple steps:

Send data from BigQuery to Google Ads

  1. Setup BigQuery as a Source
  2. Setup Google Ads as a Destination
  3. Create customer segment based on the data you have in BigQuery
  4. Select a treatment/control group
  5. Export to Google Ads

Activate BigQuery Data with GrowthLoop Today! 

Make use of your first-party data by connecting it with your advertising platforms. GrowthLoop facilitates data-driven marketing with our data activation platform. GrowthLoop is here to streamline your data-driven marketing strategies by accelerating data activation. Within minutes, activate your raw data sets to over 30 destinations, including Google Ads.

Ready to get started? Schedule your free demo with our software specialists

Published On:
May 9, 2021
Updated On:
August 14, 2024
Read Time:
5 min
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Send data from BigQuery to Google Ads in under 5 minutes

Chris Sell

Chris Sell

The data you have on your customers is incredibly valuable. The only pesky problem is it is locked away in your Cloud Data Warehouse so your business teams can’t use it easily for marketing and sales campaigns.

Today we’re going to show you how you get customer data from BigQuery to Google Ads to target your Search, Youtube, Gmail, and Display ads on Google. And we’re going to do it in less time than it takes to make a cup of coffee.

But, first, let’s take a quick step back. Why send your data to Google Ads in the first place? You may have seen some of our reasoning from our posts on other channels, but we see several common use cases across clients where bringing the power of BigQuery to Google Ads can create real business value. Some of the common use cases we see include:

  1. Best Customer Lookalikes — Exporting lists of your best customers to Google Ads to find “similar” users to acquire.
  2. Churn Re-activation — Exporting lists of customers who have churned and you are trying to win back based on what they purchased in the past.
  3. Free to Paid Conversion — Exporting lists of free sign-ups you are trying to convert to paid customers.
  4. Upsell Non-Email Openers — The majority of your customers don’t read your emails, unfortunately. For those non-readers, you can get in front of them on Google and Youtube for cross-selling and upsell to new products. This is a clever use case we see all the time.

When activating your BigQuery customer data in Google there are a few key elements to consider:

  1. One time vs Ongoing Sync — Do you want the customer segment in BigQuery to be sent once to Google Ads? Or do you want it to update every day based on which customers now fit your segment criteria (ie. churned customers). Usually, we see customers want ongoing sync, but find it difficult to implement in practice.
  2. Measurement — Are you going to hold back any portion of your customer segment as a control group to measure if Google Ads is driving incremental revenue?

With GrowthLoop you can start syncing your BigQuery data to Google Ads in a few simple steps:

Send data from BigQuery to Google Ads

  1. Setup BigQuery as a Source
  2. Setup Google Ads as a Destination
  3. Create customer segment based on the data you have in BigQuery
  4. Select a treatment/control group
  5. Export to Google Ads

Activate BigQuery Data with GrowthLoop Today! 

Make use of your first-party data by connecting it with your advertising platforms. GrowthLoop facilitates data-driven marketing with our data activation platform. GrowthLoop is here to streamline your data-driven marketing strategies by accelerating data activation. Within minutes, activate your raw data sets to over 30 destinations, including Google Ads.

Ready to get started? Schedule your free demo with our software specialists

Share on social media: 

More from the Blog

CDPs
Why a composable CDP is key to your retail media network strategy

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A retail media network (RMN) lets marketplaces monetize one of their best assets: customer data. Here’s how to drive more value from your RMN using a composable CDP.

CDPs
Why customer data platforms need to evolve to meet new industry demands

Why customer data platforms need to evolve to meet new industry demands

Find out how customer data platforms are being used by organizations and what features are essential for making full use of the technology’s potential.

CDPs
How does a composable CDP use large language models (LLM)?

How does a composable CDP use large language models (LLM)?

Learn how composable CDPs and LLMs work together for customized marketing campaign recommendations.

Looking for guidance on your Data Warehouse?

Supercharge your favorite marketing and sales tools with intelligent customer audiences built in BigQuery, Snowflake, or Redshift.

Get Demo