Customer targeting

Churn scoring

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

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 a Postgres user working at a consumer brand, Faraday's Churn scoring predictions can be just the tool you need. Imagine having a clear, data-driven way to identify which of your customers are most likely to leave, all within your familiar Postgres environment. It makes acting on this insight a bit smoother since you can integrate these predictions directly into the systems you're already using. By having this foresight, you get the chance to focus your retention efforts on the customers who need it most, helping you manage your resources more wisely. It keeps the process efficient and grounded in the data you already trust.

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