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Customer targeting
Churn scoring
Know which customers are ready to churn while there's still time to save them — using Klaviyo
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 both Faraday and Klaviyo, integrating Churn scoring predictions can be a really smart move. Imagine having a clearer insight into which of your customers are most likely to stop buying from you. With these predictions directly in Klaviyo, you can tailor your email campaigns, special offers, or engagement strategies specifically for those at-risk customers. It's a gentle way to keep them interested and connected without a lot of extra effort. Plus, it helps you make better use of the data you already have, giving your marketing efforts a nice boost.
- Step 1
Connect your data sources
Use the link below to connect Klaviyo 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. These links 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 churn scoring pipeline and deploy to Klaviyo
Finally, deploy your prediction with the link below. - Step 6
Deploy to Klaviyo
Create a deployment target using the Klaviyo connection you created above. Or, get started by simply deploying to CSV.
Deploy your churn scoring 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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Skip the ML struggle and focus on your downstream application. We have built-in sample data so you can get started without sharing yours.