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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using Azure SQL
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 Azure SQL and are a Faraday user, incorporating Repeat purchase readiness predictions into your workflow can really streamline your efforts. By knowing which customers are most ready to buy again, you can tailor your marketing campaigns more precisely and make informed decisions that resonate with your clientele. Integrating these predictions directly into Azure SQL means you can manage and analyze all of your customer data in one centralized location. This setup can help you stay organized and act quickly on valuable insights without having to juggle multiple platforms or data sources. It's a practical way to make sure you're reaching out to the right people at the right time.
- Step 1
Connect your data sources
Use the link below to connect Azure SQL 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 Azure SQL
Finally, deploy your prediction with the link below. - Step 6
Deploy to Azure SQL
Create a deployment target using the Azure SQL 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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