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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using Redshift Serverless
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 Redshift Serverless for your data warehousing and analytics, integrating Faraday's Repeat Purchase Readiness predictions can be a real game-changer. Imagine having a clear view of which customers are most likely to buy from you again, all within your existing Redshift setup. It helps streamline your marketing efforts seamlessly and can make your data-driven strategies even more effective. By having these predictions at your fingertips, you can focus your time and resources on nurturing the customers who are most ready to make another purchase. It’s a practical way to make the most of your data without having to juggle multiple tools.
- Step 1
Connect your data sources
Use the link below to connect Redshift Serverless to Faraday. You can also skip this step and use CSV files to get started instead. - 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. - 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. - 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. - Step 5
Define your repeat purchase scoring pipeline and deploy to Redshift Serverless
Finally, deploy your prediction with the link below. - Step 6
Deploy to Redshift Serverless
Create a deployment target using the Redshift Serverless connection you created above. Or, get started by simply deploying to CSV.
Deploy your repeat purchase readiness predictions to . . .
Aurora (MySQL)
AWS Aurora Postgres
Azure SQL
BigQuery
Facebook Custom Audiences
GCS
Google Ads
Google Cloud SQL (MySQL)
Google Cloud SQL (Postgres)
Google Cloud SQL (SQL Server)
HubSpot
Iterable
Klaviyo
LinkedIn Ads
MySQL
Pinterest Ads
Poplar
Postgres
RDS (MySQL)
RDS (Postgres)
RDS (SQL Server)
Recharge
Redshift
Redshift Serverless
S3
Salesforce
Salesforce Marketing Cloud
Segment
SFTP
Shopify
Snowflake
SQL Server
Stripe
The Trade Desk
TikTok
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