Customer targeting

Churn scoring

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

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

For a Faraday user, bringing churn scoring predictions into motherduck can be quite handy. If you're managing customer data in motherduck, having churn predictions integrated means you can seamlessly identify which customers might be on the verge of leaving. This can help you act in a timely manner, focusing your retention efforts on the folks who need a little extra attention. It's like having a heads-up before it's too late. Plus, working within the same platform means less hassle juggling different tools and more time understanding your customers' needs. It's a straightforward way to keep your finger on the pulse of customer satisfaction, all while staying organized in one place.

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