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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using Aurora (MySQL)
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, there's a good reason why you'd want to integrate Faraday's Repeat purchase readiness predictions directly into Aurora (MySQL). By having this data at your fingertips within your existing database setup, you can more smoothly identify customers who are likely to buy again without juggling multiple platforms. It keeps everything in one place, making your workflows more efficient and your data more actionable. This seamless integration helps you stay organized and focused on what really matters: understanding your customers and meeting their needs.
- Step 1
Connect your data sources
Use the link below to connect Aurora (MySQL) 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 Aurora (MySQL)
Finally, deploy your prediction with the link below. - Step 6
Deploy to Aurora (MySQL)
Create a deployment target using the Aurora (MySQL) 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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