Churn scores in Klaviyo

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 Klaviyo, 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 Klaviyo 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 Klaviyo using Pipelines

Getting started with churn scores in Klaviyo

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

Building predictions for churn scores in Klaviyo

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 klaviyo.

Create your pipeline for churn scores in Klaviyo

  • 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 Klaviyo". 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 Klaviyo


  • In the Deployment area, find the CSV module and click Add. Screenshot of the ready pipeline with no targets yet
  • Fill out the popup:
    • Choose the Identified option.

    • Choose Human friendly column headers.

  • Click the Next button. Screenshot of the new target form, filled out
  • Expand the Structure section of Advanced Settings
    • From the dropdown, select the klaviyo preset. Screenshot of the second page of the new target form, filled out
  • 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 Klaviyo

With your pipeline deployed, it's time to plug your churn scores into Klaviyo. Follow the steps below to see each Klaviyo contact enriched with their churn score.

Importing your churn score CSV into Klaviyo

  1. In your Faraday pipeline, click the Download CSV button under the deployment to download your churn scores as a CSV.
  2. Navigate to Klaviyo's Lists & Segments, and click create a list/segment in the upper right.
  3. Select list, as you'll need to create a list with these contacts and create the churn score property during upload.
  4. Give your list a unique name and, optionally, a tag. Click create list.
  5. Select upload contacts.

klaviyo create list

  1. You'll be taken to the field mapping screen, where you can map any fields that didn't automatically map.

klaviyo import mapping

Under the "Klaviyo field" column, key rows to map to are email and the churn score row outlined in step 7. Rows like "fdy_batch" and "dataset_id" can be skipped, as can the raw decimal score, which is generally the row before the score in step 7.

  1. In the final row, "fdy_outcome_propensity_percentile", click unmapped, and type in a unique name for this field, such as "churn score." Then, click create option.

new klaviyo property

  1. In the final column for your new churn score property, "type," change string to numeric, as scores will be numbers. This will enable you to create segments of contacts whose score is higher than a specific value.
  2. Click import review in the upper right to see the final report of columns being imported, properties being created, and columns skipped. Click start import to wrap up, and you can begin to plan 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.