Likelihood to churn


In this tutorial, we'll show you how to:

  • Create a predictive model for likelihood to churn using an outcome.

Let's dive in.

  1. You'll need a Faraday account — signup is free!

Confirm your data


Unless you’ve already created them for another quickstart or purpose, you’ll need to add the following cohorts to your account:

  • Churned customers
  • Customers

What’s a cohort?

A cohort is Faraday’s term for a commercially significant group of people — for example, a brand’s customers, leads, or even “people who bought X and Y and then cancelled.”

Cohort membership is fluid — continuously computed by Faraday — and is defined by events its members must all have experienced and/or traits its members must all share.

For example, a Customers cohort could be defined as the group of people who have all experienced a Transaction event at least once.

For more, see our docs on Cohorts, Events, Traits, and Datasets (which define how events and traits emerge from your data).


To verify, use a GET /cohorts request. Your response should look like this:

  "name": "Churned customers",
, ...}{
  "name": "Customers",
, ...}]

Make note of the IDs of the necessary cohorts.

If the required cohorts aren’t there, follow the instructions using these buttons, then return here to resume.

Configure your prediction

Create a likelihood to churn outcome


Use a POST /outcomes request:

curl --json '{
  "name": "Likelihood to churn",
  "eligible": "$CUSTOMERS_COHORT_ID"

Your outcome will start building in the background. You can proceed immediately with the next set of instructions. When your outcome is done building, you’ll get an email.