Customer targeting

Churn scoring

Know which customers are ready to churn while there's still time to save them — using Google Cloud SQL (Postgres)

You’ll need a Faraday account to use this template. It’s free to sign up and you can use sample data to start.

If you're a Faraday user working with Google Cloud SQL (Postgres), integrating churn scoring predictions directly into your database could make your life a lot easier. Having these insights right where your data lives means you can quickly identify which customers might be at risk of leaving. This enables you to take timely actions to keep them engaged. It simplifies workflows, reducing the need for complicated data transfers and allowing you to focus more on crafting effective retention strategies. Plus, it can help you make the most of both Faraday's prediction capabilities and the robust, scalable environment of Google Cloud SQL.

Step 1
Connect your data sources
Use the link below to connect Google Cloud SQL (Postgres) to Faraday. You can also skip this step and use CSV files to get started instead.
Google Cloud SQL (Postgres)
Step 2
Ingest your data into event streams
This allows Faraday to understand what your data means. These links will guide you through ingesting the data necessary to power this template.
Transaction Cancellation
Step 3
Organize your customer data
You'll create groups, called cohorts, that are the essential building blocks of Faraday and allow you to easily predict any customer behavior.
Customers Churned customers
Step 4
Declare your prediction objectives
With your cohorts defined, it's easy to instruct Faraday to predict the necessary behaviors. Follow the docs with the link below.
Likelihood to churn
Step 5
Define your churn scoring pipeline and deploy to Google Cloud SQL (Postgres)
Finally, deploy your prediction with the link below.
Churn scoring
Step 6
Deploy to Google Cloud SQL (Postgres)
Create a deployment target using the Google Cloud SQL (Postgres) connection you created above. Or, get started by simply deploying to CSV.
Google Cloud SQL (Postgres)