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 a Faraday user who's already comfortable with Postgres, integrating Repeat Purchase Readiness predictions can be a natural and seamless way to enhance your data workflow. These predictions can help you understand which of your customers are most ready to buy again, allowing you to tailor your marketing strategies more effectively. It's a straightforward way to make the most out of your existing Postgres setup without having to learn a whole new system. Plus, keeping everything within a familiar platform ensures that your team can quickly act on these insights without missing a beat.

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.
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 Postgres
Finally, deploy your prediction with the link below.
Repeat purchase scoring
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