Repeat purchasers in Iterable

Why use predictions for repeat purchasers?

Knowing which of your customers is most likely to buy again is key to keeping your customers on board and engaged to drive revenue.

The most effective way to predict likelihood to buy again is with machine learning. With machine learning, you can ingest your customer lists, predict their likelihood to buy again based on the historical data of similar shoppers, and plug your highest-scoring customers right back into your stack, no PhD required. With your repeat purchasers in your stack, you're ready to kick off a campaign to bring them closer to their next purchase.

Faraday makes predicting repeat purchasers among your customers intuitive & easy, and delivering them to any channel in your stack a breeze.

With repeat purchaser predictions in Iterable, you'll give your team the ability to focus on the customers that are most likely to come back for more.

Follow the steps below to get your repeat purchasers predictions into your Iterable account.

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

  • Organize your customer data into cohorts
  • Describe predictive models for repeat purchasers with outcomes
  • Deploy repeat purchasers predictions to Iterable using Pipelines

Getting started with repeat purchasers in Iterable

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

Requirements for this repeat purchasers recipe

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

Screenshot of the cohorts listing that includes Customers and Repeat purchasers

Building predictions for repeat purchasers in Iterable

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

Describe your repeat purchasers predictions with outcomes

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

Creating an outcome for likelihood to buy again.

Let's make an outcome for likelihood to buy again.

  • 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 repeat purchasers.
    • Leave Attrition cohort blank.
    • Skip over Trait blocking.
    • Enter a memorable name, like "Likelihood to buy again". 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 iterable.

Create your pipeline for repeat purchasers in Iterable

  • 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 buy again
    • For Population to include, choose the following:
      • A cohort representing your customers
    • For Population to exclude, choose the following:
      • A cohort representing your repeat purchasers
    • Enter a memorable name, like "Repeat purchasers in Iterable". 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 Iterable


  • 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 iterable 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 repeat purchasers predictions in Iterable

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

Creating a new audience list in Iterable

  1. In your Faraday pipeline, click the Download CSV button under the deployment to download your repeat purchase scores as a CSV.

  2. Navigate to Audience → Lists in Iterable.

If you already have a list you'd like to append your repeat purchase scores to, click Add subscribers / Modify List on that list and follow the same steps below.

  1. Click Import list in the upper right.
  2. Give your list a unique name, like "Faraday repeat purchase scores." Then, toggle update existing users only and click next.

Image of new Iterable list

  1. Click select a CSV file and choose your CSV, then click next.
  2. You'll be presented with a preview screen displaying new fields to be created, and the last of these will be for personas.

Image of Iterable mapping preview

  1. Click Upload subscribers to start the upload process.

  2. Once the list is finished processing, you can begin to plan a campaign to target only the best leads.

🔒 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.