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Personalization

Thematic personalization

Shape your creative and message to appeal to each target — using Aurora (MySQL)

You’ll need a Faraday account to use this template. It’s free to sign up and you can use sample data to start.

Aurora (MySQL) logoIf you're a Faraday user who's already using Aurora (MySQL), integrating Thematic personalization predictions can be a smooth and valuable addition. Imagine being able to craft messages and creative elements that resonate deeply with each of your target customers. By storing and managing these personalized predictions directly in Aurora, you can streamline your workflow and make data-driven decisions right from your existing database setup. This way, the sophisticated insights from Faraday are at your fingertips, enabling you to enhance your marketing efforts without disrupting your current database environment. It's a thoughtful way to better connect with your audience while keeping things simple and efficient.
  1. Step 1

    Connect your data sources

    Use the link below to connect Aurora (MySQL) to Faraday. You can also skip this step and use CSV files to get started instead.
  2. Step 2

    Ingest your data into event streams

    This allows Faraday to understand what your data means. This link will guide you through ingesting the data necessary to power this template.
  3. Step 3

    Organize your customer data

    You'll create groups, called cohorts, that are the essential building blocks of Faraday and allow you to easily predict any customer behavior.
  4. Step 4

    Declare your prediction objectives

    With your cohorts defined, it's easy to instruct Faraday to predict the necessary behaviors. Follow the docs with the link below.
  5. Step 5

    Define your content personalization pipeline and deploy to Aurora (MySQL)

    Finally, deploy your prediction with the link below.
  6. Step 6

    Deploy to Aurora (MySQL)

    Create a deployment target using the Aurora (MySQL) connection you created above. Or, get started by simply deploying to CSV.