Repeat purchase readiness

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 using AWS Aurora Postgres and you're a data scientist or engineer working for a consumer brand, plugging in Faraday's repeat purchase readiness predictions can be a handy addition. It smoothly integrates with your existing setup, helping you identify which of your customers is most likely to make another purchase soon. This can be really helpful for planning marketing campaigns or personalized outreach. Instead of casting a wide net, you can focus your efforts on those who are actually ready to buy again. It's a straightforward way to make your strategies just a bit more targeted and efficient, without overhauling your whole system.

Step 1
Connect your data sources
Use the link below to connect AWS Aurora Postgres to Faraday. You can also skip this step and use CSV files to get started instead.
AWS Aurora 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.
One-time purchasers Repeat purchasers 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.
Likelihood to buy again
Step 5
Define your repeat purchase scoring pipeline and deploy to AWS Aurora Postgres
Finally, deploy your prediction with the link below.
Repeat purchase scoring
Step 6
Deploy to AWS Aurora Postgres
Create a deployment target using the AWS Aurora Postgres connection you created above. Or, get started by simply deploying to CSV.
AWS Aurora Postgres