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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using 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.
If you're using SQL Server and want to know which of your customers is most ready to buy again, Repeat purchase readiness predictions from Faraday can be really helpful. Integrating these insights directly into your SQL Server allows you to seamlessly analyze and act on customer readiness data within the environment you’re already comfortable with. It can make your marketing and sales efforts more efficient by targeting those customers who are most likely to make another purchase. This means you can potentially increase sales without having to chase after customers who aren't quite there yet. It's about making your data work smarter for you, right where you need it.
- Step 1
Connect your data sources
Use the link below to connect 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 SQL Server
Finally, deploy your prediction with the link below. - Step 6
Deploy to SQL Server
Create a deployment target using the 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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