Facebook Ads is among the successful forms of digital advertising today with over 7 million advertisers currently registered on the platform. Additionally, 67% of digital marketers consider Facebook as the preferred social media channel for their business. Going by the 2019 Social Media Marketing report, Facebook is the preferred advertising channel for 72% of the B2C marketing companies and 43% of the B2B companies.

Be it for a single (or multiple) business ads or an entire advertising campaign, Facebook Ads are generating value for both B2B and B2C marketers. Besides that, Facebook ad data are a valuable source of rich analytics and insights for business decision making. However, processing Facebook data can be a long and expensive affair without the right automation tool. This is where Facebook Ads to BigQuery automation plays a crucial role.

As a data warehousing solution, Google BigQuery enables big data querying through its fast-to-execute SQL-like query engine that can process petabytes of data.

In the following sections, we shall evaluate how Facebook ad analytics integrates with Google BigQuery, and how Facebook Ads to BigQuery Automation works for the benefit of any business.

What is Facebook Ad Analytics?

As a business, you can rely on Facebook ads to build an online brand, drive traffic to your business website, and even encourage conversions. Want to monitor if your latest Facebook ad campaign is a success or not? You can rely on Facebook ad analytics with a set of key metrics and KPIs specifically designed for ad-related insights.

Be it for a single (or multiple) business ads or an entire advertising campaign, Facebook Ads are generating value for both B2B and B2C marketers. Besides that, Facebook ad data are a valuable source of rich analytics and insights for business decision making. The obvious next question is how and why to integrate Facebook analytics to BigQuery.

With the right tool, Facebook-generated data can be easily retrieved using the Facebook Ads Insights API. Apart from being fast and efficient, Google BigQuery (BQ) delivers accurate results from standard SQL queries.

In the following section, we shall see how Facebook Ads to BigQuery automation plays a crucial role.

What is Facebook Ads to BigQuery Automation?

The Facebook Ads to BigQuery Automation process works as an automated BigQuery job that works towards keeping the Facebook Ad data fresh and updated. Through the use of automatic increments of data fields, your developers can write scripts to integrate BigQuery with the Facebook data.

Additionally, you can configure these automated scripts to run as a cron job (or on a continuous basis) that can retrieve new Facebook data from the social networking platform.

How does the automated transfer of Facebook ad data to BigQuery help? Here are a few benefits:

Automation saves time, especially for companies with limited resources.

It is easier to scale even as the volume of your Facebook data keeps increasing with time.

Advanced analytics with BQ automation can be used to build an efficient data warehouse through the integration of multiple marketing and CRM tools.

Automated processes are more efficient than manual methods that require human intervention.

With an efficient data infrastructure and warehousing, business growth can be facilitated through predictive data modeling or natural language processing.

Here is a typical eCommerce case study that is ideal for analyzing Facebook ads with BigQuery:

The client’s objective is to extract MySQL database tables (containing WooCommerce data) daily and move them into the Google BigQuery warehouse.

Google BigQuery has been selected to extract insights from key Facebook Ad metrics including campaign-specific metrics and ad sets.

BigQuery database tables must also be compatible to be on-boarded on the Google Data Studio dashboard.

The automated BQ scripts must be created to be executed daily on Facebook data.

Finally, the Data Studio dashboard data must simplify data visualization along with the understanding of daily eCommerce activities and ad spending.

How does Facebook Ads to BigQuery Automation Work?

As a data analyst, you can pull any Facebook ad data using APIs like the Facebook Performance API or the Ads Insights API. These easy-to-use APIs can be used to various functions including creating ads and extracting data from the ads.

Before loading Facebook data into BigQuery, ensure that it is supported in either the CSV or the JSON data formats.

You can also automate the data loading of Facebook Ads to BigQuery with the help of any of the following data sources:

Google Cloud Storage

BigQuery supports the data loading from Cloud Storage in various formats including Avro, CSV (default), JSON, and ORC. You can use the Google console directly to perform this loading.

POST request

Another option is to post the data directly using JSON APIs. Facebook Performance API plays a crucial role in both loading and extraction of data into the BQ data warehouse. For instance, you can execute the HTTP POST request with the CURL or Postman tool.

Next, we shall look at some of the business benefits of Facebook Ads to BigQuery Automation.

Business Benefits of Facebook Ads to BigQuery Automation

BQ automation allows businesses to transfer their Facebook data for BQ processing in a matter of minutes. Here are some of the other business benefits of BQ automation:

Simplifies the process of data replication on Google BQ

Conventional ETL (Extract, Transfer, and Loading) tools are no longer sufficient for replicating big data on BigQuery. This is because ETL code writing is time-consuming, expensive to implement, and requires technical expertise. As a business enterprise, you can focus your resources on the value of data integrations in place of writing and modifying ETL code programs.

As compared to ETL, BQ automation tools are faster to execute for data replication. As a result, business enterprises can execute Facebook insights and conversions through faster decision making.

Enables direct data transfer to BigQuery

With BQ automation and integration with Facebook, you can directly fetch the ad data to BQ. Additionally, a BQ-enabled data warehouse can work towards storing terabytes of data, that can be pulled into any R programming or Python tool.

Getting the updated Facebook ad data.

Thanks to the automated BQ script, you can easily retrieve the new (or latest) data from the Facebook platform. This is more efficient than replicating the entire data block (or record) on the BQ warehouse, which is a longer and more resource-intensive process.

Connecting BigQuery to other data sources

BigQuery automation tools allow your data analysts to have access to other data sources including database systems and SaaS tools. For instance, BQ automation tools like Stitch allow you to connect your analytics to data sources like Amazon S3, BigCommerce, and Campaign Monitor.

Conclusion

Among the most efficient modes of digital advertising, Facebook Ads are also a rich data repository for business to understand customer behaviour and other trends. The use of data analytics in BigQuery presents a number of operational business benefits as highlighted in this article.

With the emergence of Facebook Ads to BigQuery automation process, data analysts and scientists have access to the updated Facebook ad data. This in turn, increases the value of data-driven insights thus delivering a competitive advantage.

As data analytics and business intelligence (BI) solution provider, Countants offers quality BigQuery services to its global customers. The company delivers customer value with its technical expertise in cloud-powered solutions driven by data analytics and data visualization.

Are you looking to leverage on valuable insights extracted from your Facebook ad data? Then it’s time to visit our website and get in touch with us.