Customer targeting

Churn scoring

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

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 Faraday and also happen to be an Iterable user, integrating Churn scoring predictions into Iterable could be a game-changer for you. Imagine having the ability to pinpoint which of your customers are most likely to churn directly within your marketing automation platform. With this insight, you can tailor personalized campaigns aimed specifically at retaining those at-risk customers. It’s like having a gentle nudge to help you craft more effective engagement strategies. Plus, by addressing churn proactively, you’re just making the most out of the tools you already love using.

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