Lead scores in AWS Aurora Postgres

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 AWS Aurora Postgres, 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 AWS Aurora Postgres 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 AWS Aurora Postgres 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

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

• AWS Aurora Postgres — 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 aws_aurora_postgres.

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 AWS Aurora Postgres".
• 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

AWS Aurora Postgres

• In the Deployment area, find the AWS Aurora Postgres module and click Add.
• Fill out the popup:
  • Provide the specified parameters for AWS Aurora Postgres.
  • Click Next.
  • 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, your lead scores are loaded into a CSV and ready to be plugged into your favorite marketing activation platform, where you can kick off 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.