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Personalization

Thematic personalization

Shape your creative and message to appeal to each target — using Google Cloud SQL (SQL Server)

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

Google Cloud SQL (SQL Server) logoIf you're using Google Cloud SQL (SQL Server) for your data needs and want to make your marketing really click with each of your targets, Faraday's thematic personalization predictions could be a neat addition. It's all about figuring out what kind of message and creative will resonate with your different audiences. By integrating these predictions into your SQL Server, you can seamlessly shape your campaigns directly within your existing setup. It keeps things smooth and efficient, letting you turn raw data into tailored messages that hit the mark, without the extra hassle of jumping between systems. Simple, straightforward, and just a part of your flow.
  1. Step 1

    Connect your data sources

    Use the link below to connect Google Cloud SQL (SQL Server) 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 Google Cloud SQL (SQL Server)

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

    Deploy to Google Cloud SQL (SQL Server)

    Create a deployment target using the Google Cloud SQL (SQL Server) connection you created above. Or, get started by simply deploying to CSV.