Connect Google Ads to BigQuery

Connect Google Ads to BigQuery in 2023

It is very simple to connect Google Ads to BigQuery, it can be done in a fast and easy manner with Windsor.ai.

First Step:

You need to select Google Ads as a Data Source and Grant Access to Windsor.ai.

Second Step:

You will need to go to Google BigQuery as a Destination.

Third Step:

Make sure you follow the instructions on the screen. Once you complete the setup your data will start streaming in the interval you specify. You can always come back and change the settings at a later stage.

 (Don’t forget to grant access to bq-upload@windsor-ai-bigquery.iam.gserviceaccount.com)

Fourth Step:

As a connector URL, you can use any URL providing a JSON. Either from the connectors or for example a URL with cached and transformed data.

Why Move Data From Google Ads to BigQuery?

Here are some reasons why organizations might choose to transfer their data from Google Ads to BigQuery:

1. Data consolidation: By connecting Google Ads data to BigQuery, you can centralize your advertising data with other marketing, sales, and customer data sources. This consolidation allows for comprehensive analysis and reporting across all your marketing efforts, providing a holistic view of your business performance.
2. Scalability and performance: BigQuery is a highly scalable and powerful data warehouse provided by Google Cloud. It can handle large volumes of data and execute complex queries quickly, enabling efficient analysis of your Google Ads data. This scalability ensures that you can process and analyze your advertising data effectively, even as it grows over time.
3. Advanced analytics capabilities: BigQuery offers advanced analytics capabilities, including SQL queries, machine learning, and data visualization tools. By connecting your Google Ads data to BigQuery, you can leverage these features to gain deeper insights into your advertising performance, audience segmentation, conversion tracking, and ROI analysis. This helps you make data-driven decisions and optimize your advertising strategies.
4. Real-time analysis: BigQuery supports real-time data streaming, allowing you to analyze your Google Ads data as it arrives. This capability enables you to monitor campaign performance in real-time, identify trends, and make timely adjustments to your advertising strategies. You can respond to changing market conditions or campaign dynamics promptly, maximizing the impact of your marketing efforts.
5. Custom reporting and dashboards: With BigQuery, you can create custom reports and dashboards tailored to your specific business needs. You can build interactive visualizations, track key metrics, and share insights across your organization. This empowers stakeholders to access relevant information and make data-driven decisions based on accurate and up-to-date data.
6. Integration with other tools: BigQuery integrates seamlessly with other components of the Google Cloud ecosystem, as well as with various data visualization and business intelligence tools. This integration allows you to combine and analyze data from multiple sources, perform advanced data modeling, and create comprehensive marketing analytics workflows. You can integrate your Google Ads data with CRM systems, website analytics, or other marketing platforms, enabling a deeper understanding of customer behavior and cross-channel analysis.
7. Long-term data retention: BigQuery provides long-term data retention, allowing you to store and analyze historical Google Ads data beyond the standard retention period. This capability enables you to perform trend analysis, seasonality evaluation, and year-over-year comparisons, helping you identify long-term patterns and make informed decisions based on historical performance.

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

While using BigQuery Data Transfer Service to connect Google Ads to BigQuery has its advantages, there are some limitations to consider:

1. Limited historical data transfer: The Data Transfer Service has a maximum limit of 180 days per data backfill request. If you require historical data beyond this timeframe, you would need to manually transfer the data, which can be time-consuming and may require additional technical resources.
2. Technical expertise required: Setting up and managing the data transfer process using the Data Transfer Service typically requires technical expertise. If your business teams are not tech-savvy, you may need to invest in training or allocate technical resources to handle the data transfer, which can be costly.
3. Location compatibility: BigQuery does not allow joining datasets saved in different location servers later. This means that when setting up datasets initially, you need to ensure they are in the same locations across your project. It’s important to plan and configure the locations correctly from the start as there is no option to change the location later.
4. Limited data transformation capabilities: The Data Transfer Service is primarily designed for transferring data and does not offer advanced data transformation capabilities. For example, if you need to convert timestamps from UTC to PST or perform other data transformations during the transfer process, you would need to handle these transformations separately before or after the data is transferred.
5. Limited data source support: The Data Transfer Service is specifically designed to bring data from Google products into BigQuery. If you have data from other sources such as Salesforce, Mailchimp, Intercom, or other non-Google platforms, you would need to use alternative methods or services to transfer and integrate that data into BigQuery.

Conclusion

Connecting your Google Ads property to BigQuery is an efficient way for digital marketing specialists who rely heavily on their website analytics for insights about their customer’s behavior.

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