Customer targeting

Churn scoring

Know which customers are ready to churn while there's still time to save them — using AWS Aurora 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 using AWS Aurora Postgres and you want to keep a close eye on customer retention, churn scoring predictions from Faraday could be really helpful. By integrating these predictions directly into your AWS Aurora Postgres database, you can seamlessly identify which customers are likely to leave. This allows you to take proactive steps to keep them engaged. It’s like having a gentle nudge that helps you focus your resources on the customers who need attention the most. Ultimately, this means you can make smarter, data-driven decisions without having to juggle multiple platforms or tools.

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