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Personalization
Adaptive discounting
Offer your best promos to the customers who most deserve it — using Pinterest Ads
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 Pinterest Ads to reach your audience, you might want to consider incorporating Faraday's adaptive discounting predictions to get the most out of your campaigns. It's all about being smarter with your promotions. By leveraging these predictions, you can tailor your discounts to the specific worth of each potential customer. This means offering your best deals to those who are most likely to appreciate and act on them, while being more measured with others. It’s a gentle way of optimizing your resources and ensuring your marketing efforts are as effective as possible without any extra hassle.
- Step 1
Connect your data sources
Use the link below to connect Pinterest Ads 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 Pinterest Ads
Finally, deploy your prediction with the link below. - Step 6
Deploy to Pinterest Ads
Create a deployment target using the Pinterest Ads 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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