Google Ads to BigQuery: 2 Easy Methods

Google Ads is one of the modern marketer’s favorite channels to grow the business. If you are someone who has even glanced at the Google Ads interface would know that Google provides a gazillion data points to optimize and run personalized ads. The huge amount of diverse data points available makes performance tracking a complex and time-consuming task.

Well, the complexity increases further when Businesses want to build a 360-degree understanding of how Google Ads fare in comparison to other marketing initiatives (Facebook Ads, LinkedIn Ads, etc.). To enable a detailed, convoluted analysis like this, it becomes important to extract and load the data from all the different marketing platforms used by a company to a robust cloud-based Data Warehouse like Google BigQuery. This blog talks about the different approaches to use when loading data from Google Ads to BigQuery.

What are the Methods to Connect Google Ads to BigQuery?

Here are the methods you can use to establish a connection from Google Ads to BigQuery in a seamless fashion:

• Method 1: Using LIKE.TG to Connect Google Ads to BigQuery
• Method 2: Using BigQuery Data Transfer Service to Connect Google Ads to BigQuery

Method 1: Using LIKE.TG to Connect Google Ads to BigQuery

LIKE.TG works out of the box with both Google Ads and BigQuery. This makes the data export from Google Ads to BigQuery a cakewalk for businesses. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

With LIKE.TG ’s point-and-click interface, you can load data in just two steps:

• Step 1: Configure the Google Ads data source by providing required inputs like the Pipeline Name, Select Reports, and Select Accounts.

Step 2: Configure the BigQuery destination where the data needs to be loaded by providing details like Destination Name, Dataset ID, Project ID, GCS Bucket, and Sanitize Table/Column Names.

Once this is done, your data will immediately start moving from Google Ads to BigQuery.

Method 2: Using BigQuery Data Transfer Service to Connect Google Ads to BigQuery

Before you begin this process, you would need to create a Google Cloud project in the console and enable BigQuery’s API. Also, you need to enable billing on your Google Cloud project. This is a mandatory step that needs to be executed once per project. In case you already have set up a project, you would only need to enable the BigQuery API.

1. On the BigQuery platform, hit the “Create a Dataset” button and fill out the Dataset ID and Location fields. This will create a dedicated space for storing your Google Ads data.
2. Next, enable BigQuery Data Transfer Service from the web UI. Note – you would need to have admin access to transfer and update the data.
3. Click on the “Add Transfer” button. Select “Google Ads” in the source and destination dataset.
4. BigQuery’s data connector allows you to set up refresh windows (the max offered is 30 days) and a schedule to export the Google Ads data.
5. Now, enter your Google Ads Customer ID or Manager Account (MCC).
6. Next, allow the ‘Read’ access to the Google Ads Customer ID. This is needed for the transfer configuration.
7. It is generally a good practice to opt for email notification in case a loading failure occurs.

Despite this being a native integration with two products available from Google, there are a few limitations that make companies look out for other options.

Limitations of using BigQuery Data Transfer Service to Connect Google Ads to BigQuery

• BigQuery Data Transfer Service supports a maximum of 180 days per data backfill request. This means you would have to manually transfer any historical data.
• Since the business teams that need this data are not very tech-savvy, using this approach would necessarily mean that a company would need to invest tech bandwidth to move data. This is an expensive affair.
• While transferring data, you need to remember that BigQuery doesn’t allow joining datasets saved in different location servers later. So, always create datasets in the same locations across your project. Hence, you need to be careful initially while setting up as there’s no option to change the location later.
• Say you want to convert the timestamp in the data from UTC to PST, such modifications are not supported on the BigQuery Transfer service.
• BigQuery transfer service can only bring data from Google products into BigQuery. In the future, in case you want to bring data from other sources such as Salesforce, Mailchimp, Intercom, and more, you would need to use another service.

What can you achieve by replicating data from Google Ads to BigQuery?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Google Ads to BigQuery, does your use case make the list?

• Know your customer: Get a unified view of your customer journey by combing data from all your channels and user touchpoints. Easily visualize each stage of your sales funnel and quickly derive actionable insights.   
• Supercharge your conversion rates: Leverage analysis-ready impressions, website visits, & clicks data from multiple sources in a single place. Understand what content works best for you and double down on it to increase conversions.
• Boost Marketing ROI: With detailed campaign reports at your grasp in near-real time, reallocate your budget to the most effective Ad strategy.

Conclusion

This blog talks about the different methods you can use to establish a connection in a seamless fashion: using BigQuery Data Transfer Service and a third-party tool, LIKE.TG .

Apart from providing data integration in Google Ads for free, LIKE.TG enables you to move data from a variety of data sources (Databases, Cloud Applications, SDKs, and more). These include products from both within and outside of the Google Suite.

Share your experience of replicating data! Let us know in the comments section below!

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