Customer targeting

Churn scoring

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

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

Hey there! If you're already using Faraday for its powerful customer behavior insights, adding churn scoring predictions to your Google Cloud Storage (GCS) can be a smart move. It gives you a clear view of which customers are most likely to leave, all while keeping the data in your existing Google cloud environment. This makes it easier for your data science team to integrate these insights into your existing workflows and take proactive steps to retain those at-risk customers. With everything in one place, it's a convenient way to make sure you're always a step ahead.

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