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Adaptive discounting

Offer your best promos to the customers who most deserve it — using RDS (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.

RDS (MySQL) logoIf you're a Faraday user who relies on RDS (MySQL), you might find a lot of value in using Adaptive discounting predictions right within your database. This type of prediction helps you figure out how significant of a promotion each customer deserves, which can be really useful if you're aiming to maximize the impact of your marketing efforts. By integrating these predictions directly in RDS (MySQL), you can seamlessly use your existing data infrastructure to make more informed decisions. It's convenient and keeps everything in one place, allowing you to get actionable insights without needing to switch between different tools or systems. This way, you can offer your best promotions to the customers who truly deserve them, making your marketing strategies more effective and efficient.
  1. Step 1

    Connect your data sources

    Use the link below to connect RDS (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 adaptive discounting pipeline and deploy to RDS (MySQL)

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

    Deploy to RDS (MySQL)

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