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Personalization
Adaptive discounting
Offer your best promos to the customers who most deserve it — using Redshift
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 Faraday and also have Redshift in your tech stack, integrating Adaptive discounting predictions could be a smart move for you. With Adaptive discounting, you can tailor your promotions to offer the right deals to the right customers, figuring out who deserves the best discounts. Seamlessly pulling this data into Redshift means you can leverage your existing infrastructure for faster queries and more efficient data handling. This way, you keep all your insights in one place, making it simpler to track, analyze, and act on them. No need to juggle multiple platforms; everything flows easily into your current setup, saving you time and effort.
- Step 1
Connect your data sources
Use the link below to connect Redshift 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 adaptive discounting pipeline and deploy to Redshift
Finally, deploy your prediction with the link below. - Step 6
Deploy to Redshift
Create a deployment target using the Redshift connection you created above. Or, get started by simply deploying to CSV.
Deploy your adaptive discounting 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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