Customer targeting

Churn scoring

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

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 already a fan of Faraday's powerful customer predictions and you’re also using Poplar, you'll appreciate how churn scoring can fit seamlessly into your workflow. Poplar is all about making informed, data-driven decisions, and knowing which customers are at risk of churning gives you that extra edge. By integrating churn scoring predictions, you can proactively address customer needs before they decide to leave. It's all about keeping your customers happy and engaged, making your job a bit easier and more efficient.

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