Persona assignments in AWS Aurora Postgres

Why use predictions for persona assignments?

Ah personalization, the holy grail of growth teams everywhere. Too bad it’s so difficult to do well.

The best way to personalize email is the simplest: organize your customers into personas, then produce straightforward variations of your email content for each persona. This way, you can have both copy and creative perfectly aligned with the interests of each persona so that your customers feel like you get them.

Faraday makes discovering your brand's bespoke personas intuitive & easy, and delivering them to any channel in your stack a breeze.

With persona assignment predictions in AWS Aurora Postgres, you'll give your team the ability to personalize engagement to perfection through insights from AI-generated personas.

Follow the steps below to get your persona assignments 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 prediction models for personas with persona sets
• Deploy persona assignments predictions to AWS Aurora Postgres using Pipelines

Getting started with persona assignments in AWS Aurora Postgres

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

Requirements for this persona assignments recipe

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

• 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

Building predictions for persona assignments in AWS Aurora Postgres

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

Getting started with AWS Aurora Postgres personalization

Persona sets use machine learning to organize people into unique, coherent subgroups (called personas) that you can use to personalize your outreach.

Creating a persona set for your Customers

Let's make a persona set that organizes your customers into personas.

• In the navigation sidebar, choose Persona sets.
• Click the New persona set button.
• Fill out the form:
  • For Choose a cohort, pick the cohort that best represents your customers.
  • Leave the advanced settings at their defaults.
  • Enter a memorable name, like "Customers".
• Click the Save persona set button.

Faraday will do some magic in the background, so you can proceed with the rest of the instructions. When your persona set is done building, you'll get an email, and you can review your personas.

Using Pipelines to deploy predictions to your stack

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

Create your pipeline for persona assignments in AWS Aurora Postgres

• In the navigation sidebar, choose Pipelines.
• Click the New Pipeline button.
• Fill out the form:
  • For Payload, choose the following:
    • Persona set: Customers
  • For Population to include, choose the following:
    • A cohort representing your customers
  • Enter a memorable name, like "Persona assignments 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.

Deploying your pipeline to AWS Aurora Postgres

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.
• Click the Next button.
• Skip the "Advanced Settings" by clicking the Finish button.
• 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.

How to use your persona assignments predictions in AWS Aurora Postgres

With your pipeline deployed, your persona assignments are loaded into a CSV and ready to be plugged into your favorite marketing activation platform, where you can kick off personalized campaigns.

🔒 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.