Lead scores in Klaviyo

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 Klaviyo, 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 Klaviyo 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 Klaviyo 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 klaviyo.

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 Klaviyo".
• 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 Klaviyo. Follow the steps below to see each Klaviyo contact enriched with their lead score.

Importing your lead score CSV into Klaviyo

1. In your Faraday pipeline, click the Download CSV button under the deployment to download your lead 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 lead score property during upload.

4. Give your list a unique name and, optionally, a tag. Click create list.

5. Select upload contacts.

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

🗒️ Under the "Klaviyo field" column, key rows to map to are email and the lead 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.

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

8. In the final column for your new lead 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.
9. 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 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.