Customer targeting

Churn scoring

Know which customers are ready to churn while there's still time to save them — using RDS (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 who also uses RDS (Postgres), integrating churn scoring predictions directly into your database could make your life a lot easier. Imagine having quick access to insights about which of your customers are most likely to leave, right within the same environment you already use to manage your data. Without needing to juggle multiple platforms, you can seamlessly query your churn risk metrics and use this crucial information to take timely action. It’s a straightforward way to keep everything in one place and make more informed decisions. Plus, it can help you stay ahead of potential issues before they become big problems.

Step 1
Connect your data sources
Use the link below to connect RDS (Postgres) to Faraday. You can also skip this step and use CSV files to get started instead.
RDS (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 RDS (Postgres)
Finally, deploy your prediction with the link below.
Churn scoring
Step 6
Deploy to RDS (Postgres)
Create a deployment target using the RDS (Postgres) connection you created above. Or, get started by simply deploying to CSV.
RDS (Postgres)