Understanding user behavior is critical to improving your app's engagement and retention.

Firebase, a popular mobile and web application development platform, does provide tools for app analytics. However, when it comes to diving deeper into data analysis, Firebase alone can have its limitations. This is where exporting Firebase events to BigQuery, Google's powerful data warehouse and analytics platform, comes into play.

In this guide, we will explore why exporting Firebase events to BigQuery is essential and how to set up this integration. By the end of this article, you'll understand the benefits of this powerful combination and the steps required to make it work for your app analytics needs.

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Why Export Firebase Events to BigQuery?

Firebase is known for its user-friendly analytics features, allowing app developers to track data such as user counts, demographics, and basic event counts. While these features are valuable, they only scratch the surface of what you can achieve with your app data. Exporting Firebase events to BigQuery opens up a world of possibilities, including:

1. Advanced Data Analysis:

Once your Firebase events are in BigQuery, you can perform more advanced data analysis. You can ask questions like:

  • What is the average revenue generated by specific user segments?
  • What are the most common event sequences leading to user conversions?
  • How do user behaviors differ across different geographic regions?

To gather even more insights into your users' behaviors.

2. Custom Analytics:

BigQuery enables you to create custom analyses tailored to your specific needs. You can define custom metrics, create complex queries, and generate insights unique to your app's goals and objectives.

3. Predictive Modeling:

BigQuery offers built-in machine learning functions, making it relatively easy to build predictive models on user behavior. You can forecast user actions, predict churn rates, and make data-driven decisions to enhance user engagement and retention.

4. Integration with tools like LookerStudio:

BigQuery has a seamless integration with tools like LookerStudio, a powerful data exploration and visualization platform. Integrations like these allow you to do things like build custom dashboards and reports on top of your Firebase event data without the need for extensive data engineering support.

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Image courtesy of Google

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Setting Up Firebase to BigQuery Export

Now that you understand why exporting Firebase events to BigQuery is beneficial, let's dive into the steps required to set up this integration:

Note: These steps can also be found here.

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Step 1. Go to the Integrations page in the Firebase console.

Step 2. In the BigQuery card, click Link.

Step 3. After having read the details on the first page, click Next.

Step 4. Toggle Google Analytics (this will enable exporting your app's click stream data).

Step 5. Select the GCP region where you wish this data to be stored.

Step 6. Toggle Cloud Messaging (this will enable exporting your push event data).

Step 7. Select the GCP region where you wish this data to be stored.

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These steps are based on: https://firebase.google.com/docs/projects/bigquery-export

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Wondering what to do with your data once it's in BigQuery?

Data by itself is a cost. Data needs to be used effectively to add value, but according to AWS Executive Insights, a staggering share of 97% data currently sits unused.

That's why we created Aampe.

‍Aampe is a data optimization platform that orchestrates your various user messaging across channels like Push, SMS, WhatsApp, In-App, and more based on observed patterns and user behaviors from your event data that's stored in BigQuery, and it can be activated in a few simple steps:

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Step 1: Provide Aampe with Access to your Firebase BigQuery Export

There are 2 ways to send data to Aampe from BigQuery

  1. Direct read access from your BQ dataset [Link], or
  2. Push to Google Cloud Storage bucket [Link]

(More detailed instructions here: https://docs.aampe.com/docs/bigquery#1-direct-read-access-from-your-bq-dataset)

Step 2: Create service account credentials so Aampe will be able to trigger push notifications on your behalf

This will allow Aampe to call the Firebase Cloud Messaging API on your behalf. (Don't have Firebase? It's cool. We work with all the major customer engagement platforms.)

Estimated setup time, 1 hour. Full instructions here: https://docs.aampe.com/docs/firebase-cloud-messaging

Step 3: Configure your credentials in Aampe

This step involves adding your Firebase App Name and JSON Credentials on this page:

Step 4: Set Up Daily Data Dump

To make the integration work smoothly, you'll need to set up a daily job that dumps relevant data into a shared S3 bucket. Follow these steps:

  1. Create an S3 bucket if you don't already have one
  2. Provide Aampe with key access to this S3 bucket.
  3. Configure a daily job that extracts essential data, such as user IDs and FCM (Firebase Cloud Messaging) notification tokens, and stores it in the shared S3 bucket.

Note: If you need help with this step, please reach out and we can help you create a bucket.

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That's it!

Now you'll be using your data more effective than 98% of the companies out there.

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Conclusion

Exporting Firebase events to BigQuery is a game-changer for app developers and businesses seeking to gain deeper insights from their app data. By combining Firebase's user-friendly analytics with BigQuery's advanced capabilities, you can unlock a treasure trove of information, customize your analytics, and make data-driven decisions that enhance your app's performance and user experience.

Adding Aampe allows you to intelligently action on this data without requiring extensive resources on your side.

Follow the steps outlined in this guide to set up the integration and harness the full potential of your app's data. With the right tools and strategies in place, you can efficiently meet the evolving needs of your users.