Personalization

Thematic personalization

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

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

Many Faraday users also rely on Google Cloud SQL (Postgres) for their data management, and integrating Thematic personalization predictions into this environment can be incredibly helpful. By storing your predictions in Postgres, you can easily join them with your existing customer data, making it simpler to shape your creative and messaging to appeal to each target audience. This seamless integration can help streamline your workflows, keeping everything in one place and easy to access. Plus, leveraging the power of AI to fine-tune your marketing efforts can lead to more meaningful customer interactions, all while utilizing a familiar and robust database system like Postgres.

Step 1
Connect your data sources
Use the link below to connect Google Cloud SQL (Postgres) to Faraday. You can also skip this step and use CSV files to get started instead.
Google Cloud SQL (Postgres)
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.
Transaction
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.
Customers
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.
Customer personas
Step 5
Define your content personalization pipeline and deploy to Google Cloud SQL (Postgres)
Finally, deploy your prediction with the link below.
Content personalization
Step 6
Deploy to Google Cloud SQL (Postgres)
Create a deployment target using the Google Cloud SQL (Postgres) connection you created above. Or, get started by simply deploying to CSV.
Google Cloud SQL (Postgres)