Customer targeting

Churn scoring

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

You will need a Faraday account to use this template. It is free to sign up and you will just need some sample data to start.

Get started for free

If you're using Databricks and you're concerned about customer retention, incorporating Faraday's churn scoring predictions can be a smart move. By having this predictive insight directly in Databricks, you can swiftly identify which of your customers are most likely to leave. This means you have the chance to engage with them effectively and improve retention efforts before it's too late. It's all about making informed, timely decisions without needing to juggle data between platforms. Plus, it complements your existing analytics workflow seamlessly, helping you keep the focus on building meaningful customer relationships.

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