Faraday

Faraday ingests historical customer transaction and engagement data through a no-code interface, applies pre-trained or auto-tuned machine learning models to identify customers at risk of churning, and surfaces risk scores ranked by confidence. The platform abstracts away feature engineering and model selection, allowing non-technical users to generate churn predictions by connecting data sources and selecting a prediction horizon (e.g., 30/60/90 days), then visualizing results in a dashboard with actionable segments.

Faraday processes historical customer revenue, purchase frequency, and retention patterns to forecast the total expected revenue each customer will generate over a specified time horizon (e.g., 12 months). The platform uses regression or survival analysis models to predict LTV by learning patterns from cohorts of similar customers, then ranks customers by predicted value to enable prioritization of acquisition, upsell, and retention efforts.

Faraday provides a no-code interface to connect customer data from multiple sources (CSV uploads, Stripe, Shopify, databases, data warehouses) and automatically normalizes fields (customer ID, transaction date, revenue) into a unified schema. The platform handles data validation, deduplication, and missing value imputation so that non-technical users can prepare data for prediction without SQL or ETL tools.

Faraday automatically segments customers into cohorts based on predicted churn risk, LTV, and behavioral patterns (e.g., purchase frequency, product usage), then visualizes these segments in a dashboard with actionable metrics (size, average LTV, churn rate). Users can filter and export segments to downstream tools (CRM, email marketing, ad platforms) for targeted campaigns without manual SQL queries.

Faraday automatically selects, trains, and retrains machine learning models (e.g., logistic regression, gradient boosting, neural networks) on uploaded customer data without user intervention. The platform uses techniques like cross-validation and hyperparameter optimization to find the best-performing model for each prediction task (churn, LTV), then schedules periodic retraining as new data arrives to maintain prediction accuracy over time.

Faraday provides a web-based dashboard that visualizes churn risk, LTV forecasts, and customer segments through charts, tables, and interactive filters. Users can drill down into specific customer cohorts, compare metrics across time periods, and export reports without writing SQL or using BI tools. The dashboard updates automatically as new predictions are generated.

Faraday exports customer segments and prediction scores to downstream tools (Salesforce, HubSpot, Mailchimp, Klaviyo) via API integrations or CSV uploads, enabling users to trigger automated campaigns based on churn risk or LTV without manual data transfer. The platform supports bi-directional sync in some cases, updating customer records with prediction scores as new models are trained.

Faraday offers a free tier that allows users to ingest data, generate churn and LTV predictions, and create segments without providing a credit card or payment information. The free tier is designed to lower barriers for early-stage startups and SMBs to access predictive analytics, though it likely includes constraints on data volume, prediction frequency, and feature access.