Customer targeting

Churn scoring

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

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

Sure, here's a paragraph that meets those criteria: If you're a Faraday user who also uses Azure SQL, integrating Churn scoring predictions can make a big difference in managing customer relationships. By having these predictions directly within your current database setup, you can easily identify which of your customers are most at risk for churning. This means you can take timely actions to engage and retain them without disrupting your workflow. It's a straightforward way to leverage powerful AI insights without needing to juggle multiple platforms or tools. Plus, it helps keep everything streamlined and efficient, so you can focus on what matters most—keeping your customers happy.

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