Customer targeting

Churn scoring

Know which customers are ready to churn while there's still time to save them — using Redshift Serverless

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 using Faraday for churn scoring predictions and also work with Redshift Serverless, integrating the two could be quite handy. Churn scoring can help you identify which of your customers are most at risk of leaving, giving you a chance to take action before it's too late. By bringing these predictions into Redshift Serverless, you can streamline your data processing and keep everything in one place. This makes it easier to manage and analyze your customer data, all while leveraging the scalability and flexibility of Redshift Serverless. It's a straightforward way to make your data work harder for you without any extra hassle.

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