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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using Facebook Custom Audiences
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 Facebook Custom Audiences and you want to make your campaigns more effective, our Repeat Purchase Readiness predictions could be a great help. These predictions identify which of your customers are most likely ready to buy again. By integrating this insight with Facebook Custom Audiences, you can target ads to those customers more precisely. It means you can spend your advertising budget more wisely by focusing on those who are more likely to make another purchase soon. It’s a smart way to keep your customers engaged and potentially boost your repeat sales with a bit less guesswork.
- Step 1
Connect your data sources
Use the link below to connect Facebook Custom Audiences 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 Facebook Custom Audiences
Finally, deploy your prediction with the link below. - Step 6
Deploy to Facebook Custom Audiences
Create a deployment target using the Facebook Custom Audiences 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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