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.

Sure thing. If you're a Faraday user who's also comfortable with Postgres, integrating Adaptive discounting predictions directly into your database can be a smooth move. It allows you to leverage your existing database skills while making smarter promotional decisions based on AI insights. Instead of taking a one-size-fits-all approach, you'll be able to identify which customers are truly worth giving that extra discount to, all from within Postgres. This means you can keep your workflow streamlined, data in one place, and easily run queries to fine-tune your promotional strategies. It's about working smarter, not harder, and making your promotional efforts more effective without complicating your process.

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