High spenders in Iterable

Why use predictions for high spenders?

Knowing which of your leads and prospects are most likely to spend big gives you the opportunity to serve them just the right offers at just the right time to drive revenue.

The most effective way to predict high-spend customers is with machine learning. With machine learning, you can constantly keep your lists of leads and prospects up-to-date with high-spend predictions based on the historical data of similar shoppers, and plug your high spenders right back into your stack, no PhD required. With your high spenders in your stack, you're ready to kick off a campaign to drive them to conversion.

Faraday makes predicting your high spenders intuitive & easy, and delivering them to any channel in your stack a breeze.

With high spend score predictions in Iterable, you'll give your team the ability to target not just those likely to become customers, but those most likely to spend big.

Follow the steps below to get your high spenders predictions into your Iterable account.

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

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

Getting started with high spenders in Iterable

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

Requirements for this high spenders recipe

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

Screenshot of the cohorts listing that includes Customers and High spenders

Building predictions for high spenders in Iterable

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

Describe your high spenders predictions with outcomes

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

Creating an outcome for likelihood for high spend.

Let's make an outcome for likelihood for high spend.

  • 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 high spenders.
    • Leave Attrition cohort blank.
    • Skip over Trait blocking.
    • Enter a memorable name, like "Likelihood for high spend". 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 high spenders 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 for high spend
    • For Population to include, choose the following:
      • A cohort representing your customers
    • For Population to exclude, choose the following:
      • A cohort representing your high spenders
    • Enter a memorable name, like "High spenders 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 high spenders predictions in Iterable

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

Creating a new audience list in Iterable

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

Optionally, you can open your CSV in Microsoft Excel, Google Sheets, or any other CSV editor, then modify the column header for your high spend scores to be more human-friendly–"Faraday high spend score," for example.

  1. Navigate to Audience → Lists in Iterable.

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

  2. Click Import list in the upper right.

  3. Give your list a unique name, like "Faraday high spend scores." Then, toggle update existing users only and click next.

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.

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 the high spenders in this list.

🔒 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.