Adaptive discounting

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

If you're a Faraday user who also takes advantage of Iterable, integrating Adaptive discounting predictions can really streamline your promo strategy. Essentially, it helps you figure out how much of a discount to offer each customer based on their potential response, making sure you don't over or under-sell your promotions. This way, you can allocate your discounts more effectively and make your marketing campaigns in Iterable more precise. It's a pretty practical way to ensure your best offers are going to the right people, helping you get more out of your promotional efforts without the guesswork.

Step 1
Connect your data sources
Use the link below to connect Iterable to Faraday. You can also skip this step and use CSV files to get started instead.
Iterable
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.
Forecasted spend
Step 5
Define your adaptive discounting pipeline and deploy to Iterable
Finally, deploy your prediction with the link below.
Adaptive discounting
Step 6
Deploy to Iterable
Create a deployment target using the Iterable connection you created above. Or, get started by simply deploying to CSV.
Iterable