Customer targeting

Churn scoring

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

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 works with Snowflake, incorporating Churn scoring predictions into your Snowflake environment can be really valuable. It enables you to seamlessly identify which customers are most at risk of churning without switching between platforms. With the data already in Snowflake, you can get actionable insights directly within your existing workflow, helping you to address potential churn issues more efficiently. Plus, keeping everything in one place simplifies data management and analysis, making your job a bit easier and your strategies more effective.

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