Customer targeting

Churn scoring

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

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 Redshift and also a Faraday user, integrating churn scoring predictions can be a game-changer for your customer retention strategy. Imagine having the ability to seamlessly access insights about which of your customers are most likely to leave, right within your data warehouse. This means you can quickly analyze and act on these predictions alongside your other data, making it easier to craft targeted campaigns or outreach efforts. It's all about efficiency and staying proactive, so you can address potential churn before it happens, effortlessly fitting these insights into your existing workflow.

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