Lead scores in Iterable

Introduction

Knowing which leads are worth chasing–and which aren't–is key to keeping your teams focused and efficient in driving revenue.

The most effective way to predict a lead's likelihood to buy is with machine learning. With machine learning, you can ingest your lead lists as they come in, predict their likelihood to buy based on the historical data of similar shoppers, and plug the highest-scoring leads right back into your stack, no PhD required. No more time wasted on leads that were never going to convert in the first place.

Faraday makes predicting likelihood to buy for your leads intuitive & easy, and delivering them to any channel in your stack a breeze.

With lead score predictions in Iterable, you'll give your team the ability to focus on only the leads most likely to convert.

Follow the steps below to get your lead scores predictions into your Iterable account.

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

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

Getting started

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

Prerequisites

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

• A cohort representing your leads — or create one first
• A cohort representing your customers — or create one first

Objectives

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

Outcomes

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

Likelihood to convert

Let's make an outcome for likelihood to convert.

• In the navigation sidebar, choose Outcomes.
• Click the New outcome button.
• Fill out the form:
  • For Eligibility cohort, pick the cohort that best represents your leads.
  • For Attainment cohort, pick the cohort that best represents your customers.
  • Leave Attrition cohort blank.
  • Skip over Trait blocking.
  • Enter a memorable name, like "Likelihood to convert".
• 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.

Pipeline

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

Create your pipeline

• In the navigation sidebar, choose Pipelines.
• Click the New Pipeline button.
• Fill out the form:
  • For Payload, choose the following:
    • Outcome: Likelihood to convert
  • For Population to include, choose the following:
    • A cohort representing your leads
  • Enter a memorable name, like "Lead scores in Iterable".
• Click the Save pipeline button.

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

Deploy your pipeline

CSV

• In the Deployment area, find the CSV module and click Add.
• Fill out the popup:
  • Choose the Identified option.
  • Skip the advanced settings.
• Click the Finish button.
• Click the Test deployment button and confirm the results meet your expectations. 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.

Conclusion

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

Creating a new audience list in Iterable

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

2. Navigate to Audience → Lists in Iterable.

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

3. Click Import list in the upper right.
4. Give your list a unique name, like "Faraday lead scores." Then, toggle update existing users only and click next.

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

7. Click Upload subscribers to start the upload process.

8. 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.