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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using RDS (SQL Server)
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 also works with RDS (SQL Server), having Repeat purchase readiness predictions in your database can be a real boost. It lets you directly integrate powerful insights into your existing workflows without having to juggle multiple systems. By knowing which of your customers are most likely to buy again, you can tailor your marketing efforts and inventory forecasts more accurately. Plus, having these predictions in RDS means you can run custom queries and reports that fit right into your usual routine. It's not about transforming everything overnight, but making your data work a bit smarter for you.
- Step 1
Connect your data sources
Use the link below to connect RDS (SQL Server) 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 RDS (SQL Server)
Finally, deploy your prediction with the link below. - Step 6
Deploy to RDS (SQL Server)
Create a deployment target using the RDS (SQL Server) 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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