Churn scores in Postgres

Introduction

Knowing which of your customers is most likely to churn gives you the opportunity to act before they make that critical decision.

The most effective way to predict likelihood to churn is with machine learning. With machine learning, you can constantly keep your list of customers up-to-date with churn scores based on the historical data of similar shoppers, and plug your likely-to-churn customers right back into your stack, no PhD required. With churn scores in your stack, you're primed to jump in, offer a discount, a helping hand, or some other offer to keep them on board.

Faraday makes predicting churn scores for your customers intuitive & easy, and delivering them to any channel in your stack a breeze.

With churn score predictions in Postgres, you’ll give your team valuable insight to intervene before the customer makes that critical decision, exactly where and when they need it.

Follow the steps below to get your churn scores predictions into your 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 churn scores with outcomes
• Deploy churn scores predictions to 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 customers — or create one first
• A cohort representing your churned customers — or create one first

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

• 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 churn

Let's make an outcome for likelihood to churn.

• 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 customers.
  • For Attainment cohort, pick the cohort that best represents your churned customers.
  • Leave Attrition cohort blank.
  • Skip over Trait blocking.
  • Enter a memorable name, like "Likelihood to churn".
• 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 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 churn
  • For Population to include, choose the following:
    • A cohort representing your customers
  • Enter a memorable name, like "Churn scores in 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

Postgres

• In the Deployment area, find the Postgres module and click Add.
• Fill out the popup:
  • Provide the specified parameters for 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 churn scores are loaded into PostgreSQL and ready to be plugged into your favorite marketing activation platform, where you can kick off a campaign to intervene just in time to keep your customers on board.

🔒 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.