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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using Google Cloud SQL (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 already using Google Cloud SQL (Postgres) for your data, incorporating Faraday's Repeat purchase readiness predictions could be a smart move. These predictions can integrate seamlessly with your existing setup, helping you pinpoint which customers are primed to make another purchase. This means you can make data-driven decisions without changing your workflow. You'll have a clearer picture of your customer base directly within your familiar database environment, making it easier to target your marketing efforts and potentially boost your sales. Plus, having everything in one place just simplifies the process.
- Step 1
Connect your data sources
Use the link below to connect Google Cloud SQL (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 Google Cloud SQL (Postgres)
Finally, deploy your prediction with the link below. - Step 6
Deploy to Google Cloud SQL (Postgres)
Create a deployment target using the Google Cloud SQL (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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