Learn how Thirstie uses embedded machine learning predictions to give users more valuable insights about their markets.
How Faraday works
- Churn scores in Azure SQL
- Churn scores in BigQuery
- Churn scores in GCS
- Churn scores in hosted CSV
- Churn scores in HubSpot
- Churn scores in Iterable
- Churn scores in Klaviyo
- Churn scores in MySQL
- Churn scores in Poplar
- Churn scores in Postgres
- Churn scores in Redshift
- Churn scores in S3
- Churn scores in Salesforce
- Churn scores in Segment
- Churn scores in Snowflake
- High spenders in Azure SQL
- High spenders in BigQuery
- High spenders in GCS
- High spenders in hosted CSV
- High spenders in HubSpot
- High spenders in Iterable
- High spenders in Klaviyo
- High spenders in MySQL
- High spenders in Poplar
- High spenders in Postgres
- High spenders in Redshift
- High spenders in S3
- High spenders in Salesforce
- High spenders in Segment
- High spenders in Snowflake
- Lead scores in Azure SQL
- Lead scores in BigQuery
- Lead scores in GCS
- Lead scores in hosted CSV
- Lead scores in HubSpot
- Lead scores in Iterable
- Lead scores in Klaviyo
- Lead scores in MySQL
- Lead scores in Poplar
- Lead scores in Postgres
- Lead scores in Redshift
- Lead scores in S3
- Lead scores in Salesforce
- Lead scores in Segment
- Lead scores in Snowflake
- Likely buyers in Ads
- Likely buyers in Azure SQL
- Likely buyers in BigQuery
- Likely buyers in Facebook
- Likely buyers in GCS
- Likely buyers in Google
- Likely buyers in hosted CSV
- Likely buyers in Linkedin
- Likely buyers in Liveramp
- Likely buyers in MySQL
- Likely buyers in Pinterest
- Likely buyers in Poplar
- Likely buyers in Postgres
- Likely buyers in Redshift
- Likely buyers in S3
- Likely buyers in Snowflake
- Likely buyers in Taboola
- Likely buyers in Youtube
- Personalized messaging in Azure SQL
- Personalized messaging in BigQuery
- Personalized messaging in GCS
- Personalized messaging in hosted CSV
- Personalized messaging in HubSpot
- Personalized messaging in Iterable
- Personalized messaging in Klaviyo
- Personalized messaging in MySQL
- Personalized messaging in Poplar
- Personalized messaging in Postgres
- Personalized messaging in Redshift
- Personalized messaging in S3
- Personalized messaging in Salesforce
- Personalized messaging in Segment
- Personalized messaging in Snowflake
First, choose your recipe
AI only matters when it’s making something you already do work better. Pick from 100s of built-in recipes, or get creative and start from scratch.
Sync customer data from Snowflake
Detect customer personas
Next, connect your data
Predicting customer behavior starts with customer data. Connect Faraday to wherever your data lives, then organize it into groups we call cohorts.
Sync customer data from Snowflake
Detect customer personas
Apply to all customers
Now define your objectives
What are you trying to predict? Use an intuitive, point-and-click interface to map your cohorts into prediction objectives.
Sync customer data from Snowflake
Detect customer personas
Apply to all customers
Deploy to Klaviyo every night
And deploy a pipeline
Finally, choose who to make these predictions on and where they should be deployed. Faraday takes care of the rest from here on out.
Big ideas? Faraday helps you build the business of your dreams
See how practical, powerful AI gives brands everywhere the boost they need to beat their goals
Iterable interviews Faraday's CEO to unlock the secrets to automated personalization using consumer data and AI responsibly.
Using Faraday’s dynamic lead scores, Sealed was able to pre-qualify a new segment of leads that was previously not working, leading to a 5% increase in leads that marketing can deliver to sales.
Along with improvements in performance, Burrow gained a better understanding of which audience segments would provide a higher returns on their ad spend.
Make your first prediction
FAQs
Faraday unifies your customer data with hundreds of rich, built-in consumer data points to provide powerful predictions that you can be confident in–in just a few clicks.
Let’s get predictive
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