How to Analyze Facebook Ads using Google Big Query Automation
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Overview • What is Facebook Ad Analytics? • What is Facebook Ads to Big Query Automation? • How does the automated transfer of Facebook ad data to BigQuery help • How does Facebook Ads to Big Query Automation Work? • Business Benefits of Facebook Ads to Big Query Automation • Conclusion
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.
With the right tool, Facebookgenerated data can be easily retrieved using the Facebook Ads Insights API. Apart from being fast and efficient, Google Big Query (BQ) delivers accurate results from standard SQL queries.
What is Facebook Ads to BigQuery Automation?
The Facebook Ads to Big Query Automation process works as an automated Big Query job that works towards keeping the Facebook Ad data fresh and updated. Using automatic increments of data fields, developers can write scripts to integrate Big Query with the Facebook data.
How does the automated transfer of Facebook ad data to BigQuery help?
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Automation saves time, especially for companies with limited resources.
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It is easier to scale even as the volume of your Facebook data keeps increasing with time.
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Advanced analytics with BQ automation can be used to build an efficient data warehouse through the integration of multiple marketing and CRM tools.
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Automated processes are more efficient than manual methods that require human intervention.
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With an efficient data infrastructure and warehousing, business growth can be facilitated through predictive data modeling or natural language processing.
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: •
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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.
Business Benefits of Facebook Ads to BigQuery Automation
1. Simplifies the process of data replication on Google BQ
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.
2. 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.
3. 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
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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 behavior and other trends. The use of data analytics in Big Query presents several operational business benefits as highlighted in this article. •With the emergence of Facebook Ads to Big Query 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.