Churn scores in Snowflake

Why use predictions for churn scores?

Knowing which of your customers is most likely to churn gives you the opportunity to act before they make that critical decision.

The most effective way to predict likelihood to churn is with machine learning. With machine learning, you can constantly keep your list of customers up-to-date with churn scores based on the historical data of similar shoppers, and plug your likely-to-churn customers right back into your stack, no PhD required. With churn scores in your stack, you're primed to jump in, offer a discount, a helping hand, or some other offer to keep them on board.

Faraday makes predicting churn scores for your customers intuitive & easy, and delivering them to any channel in your stack a breeze.

With churn score predictions in Snowflake, you’ll give your team valuable insight to intervene before the customer makes that critical decision, exactly where and when they need it.

Follow the steps below to get your churn scores predictions into your Snowflake account.


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

  • Organize your customer data into cohorts
  • Describe predictive models for churn scores with outcomes
  • Deploy churn scores predictions to Snowflake using Pipelines

Getting started with churn scores in Snowflake

Make sure you have a Faraday account (signup is free!) and that it's not in test mode.

Requirements for this churn scores recipe

You'll need the following cohorts available in your Faraday account:

Screenshot of the cohorts listing that includes Customers and Churned customers You'll also need the following connections available in your Faraday account:

Screenshot of the connections listing that includes Snowflake

Building predictions for churn scores in Snowflake

Now you'll create the prediction objective(s) necessary to complete this use case with Faraday.

Describe your churn scores predictions with outcomes

Outcomes use machine learning to predict whether or not people will exhibit a certain behavior.

Creating an outcome for likelihood to churn.

Let's make an outcome for likelihood to churn.

  • In the navigation sidebar, choose Outcomes. Screenshot of the outcomes list
  • Click the New outcome button.
  • Fill out the form:
    • For Eligibility cohort, pick the cohort that best represents your customers.
    • For Attainment cohort, pick the cohort that best represents your churned customers.
    • Leave Attrition cohort blank.
    • Skip over Trait blocking.
    • Enter a memorable name, like "Likelihood to churn". Screenshot of the new outcome form, filled out
  • Click the Save outcome button.

Faraday will do some magic in the background, so you can proceed with the rest of the instructions. When your outcome is done building, you'll get an email, and you can review your outcome.

Using Pipelines to deploy predictions to your stack

Now you'll configure the pipeline that deploys your predictions to snowflake.

Create your pipeline for churn scores in Snowflake

  • In the navigation sidebar, choose Pipelines. Screenshot of the pipelines list
  • Click the New Pipeline button.
  • Fill out the form:
    • For Payload, choose the following:
      • Outcome: Likelihood to churn
    • For Population to include, choose the following:
      • A cohort representing your customers
    • Enter a memorable name, like "Churn scores in Snowflake". Screenshot of the new pipeline form, filled out
  • Click the Save pipeline button.

Your pipeline will start building in the background. You can proceed immediately with the next set of instructions.

Deploying your pipeline to Snowflake

Snowflake

  • In the Deployment area, find the Snowflake module and click Add. Screenshot of the ready pipeline with no targets yet
  • Fill out the popup:
    • Provide the specified parameters for Snowflake.
    • Click Next.
    • Choose the Identified option.
  • Click the Next button. Screenshot of the new target form, filled out
  • Skip the "Advanced Settings" by clicking the Finish button.
  • Click the Finish button.
  • Click the Test deployment button and confirm the results meet your expectations. Screenshot of a target after hitting its test button the first time Faraday will finish building your pipeline in the background. When it's done, you'll get an email—return to the pipeline and click the Enable pipeline button to activate it.

How to use your churn scores predictions in Snowflake

With your pipeline deployed, your churn scores are loaded into Snowflake and ready to be plugged into your favorite marketing activation platform, where you can kick off a campaign to intervene just in time to keep your customers on board.

🔒 It's a best practice to permanently delete any file that contains personally identifiable information (PII) after use. Any deployment from Faraday that is unhashed contains PII, and should be deleted after uploading it to your destination for security purposes.