Hey HN — built this because every churn prediction notebook on GitHub
uses the Kaggle Telco dataset and outputs a confusion matrix that no
founder can act on.
ChurnGuard connects to your Stripe account, engineers behavioral
features from real billing data, trains an XGBoost model on your
actual churned vs retained customers, and uses SHAP to explain
why each customer is at risk in plain English.
The LLM layer (Groq free tier) generates a specific 30-day retention
playbook per at-risk customer — not "schedule a call" but actual
messaging, offer amounts, and who should own each action.
Entire stack is free. Works in Google Colab. Happy to answer
questions about the architecture.
ChurnGuard connects to your Stripe account, engineers behavioral features from real billing data, trains an XGBoost model on your actual churned vs retained customers, and uses SHAP to explain why each customer is at risk in plain English.
The LLM layer (Groq free tier) generates a specific 30-day retention playbook per at-risk customer — not "schedule a call" but actual messaging, offer amounts, and who should own each action.
Entire stack is free. Works in Google Colab. Happy to answer questions about the architecture.