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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using RDS (Postgres)
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 RDS (Postgres) and you're curious about which of your customers are ready to make their next purchase, integrating Faraday's repeat purchase readiness predictions could be a nifty addition. It works seamlessly with your existing database, providing valuable insights right where your data lives. By knowing who among your customers is most prepared to buy again, you can tailor your marketing efforts more effectively and give your sales strategy a nice little boost. It's straightforward to set up and can enhance your understanding of customer behavior without major changes to your current workflow. It's just a practical, efficient way to get more out of your customer data.
- Step 1
Connect your data sources
Use the link below to connect RDS (Postgres) 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 (Postgres)
Finally, deploy your prediction with the link below. - Step 6
Deploy to RDS (Postgres)
Create a deployment target using the RDS (Postgres) 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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