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Customer targeting
Repeat purchase readiness
Know which customers are ready to buy again — using SFTP
You’ll need a Faraday account to use this template. It’s free to sign up and you can use sample data to start.
As a Faraday user, you might find Repeat Purchase Readiness predictions delivered via SFTP incredibly handy for streamlining your workflow. If you're managing lots of customer data, having these insights fed directly into your system can save you a ton of time. You'll be able to seamlessly integrate predictions about which customers are most likely ready to buy again into your existing data operations. This means you can quickly act on those insights without needing to jump between different tools or manually download and upload files. It's a straightforward way to keep everything running smoothly and efficiently. And in the end, it helps you stay more connected to your customers' needs, right when they're ready to re-engage.
- Step 1
Connect your data sources
Use the link below to connect SFTP 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 SFTP
Finally, deploy your prediction with the link below. - Step 6
Deploy to SFTP
Create a deployment target using the SFTP 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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