All templates
Personalization

Rep assignment

Assign each lead or customer to the rep that will handle them best — using Redshift Serverless

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

Redshift Serverless logoIf you're a Faraday user working with Redshift Serverless, you might find Rep assignment predictions particularly handy for streamlining your sales process. Imagine having an intuitive way to match each lead or customer with the rep best suited to engage them—right within your Redshift Serverless environment. This can help you make the most of your data without having to juggle multiple platforms. It’s a straightforward way to enhance your team's efficiency and effectiveness, ensuring that your reps are focusing their efforts where they can make the biggest impact. Plus, integrating it with Redshift Serverless can simplify your workflow, making the whole process feel a little more seamless.
  1. Step 1

    Connect your data sources

    Use the link below to connect Redshift Serverless 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. These links 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 rep assignment pipeline and deploy to Redshift Serverless

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

    Deploy to Redshift Serverless

    Create a deployment target using the Redshift Serverless connection you created above. Or, get started by simply deploying to CSV.