Overview
A B2B SaaS company was losing 15–20% of customers per quarter with no advance warning. By the time the sales team detected a churning customer, it was usually too late — the customer had already decided or signed with a competitor.
Ventra Rocket built an AI system that detects early churn signals, giving the sales team 30–60 days to intervene before customers leave.
Challenge
Customer data was scattered across multiple systems: CRM (Salesforce), email (Outlook), social media, and the transaction database. No system connected all of this for a 360° view of each customer's health.
Additionally, every customer behaved differently — there was no single fixed threshold that defined "this customer is about to churn." The AI needed to learn patterns from history to predict each specific case.
Solution
Data Aggregation Pipeline: Connects and syncs data from Salesforce CRM, Outlook email, social media monitoring, and the transaction database into a unified data warehouse (PostgreSQL + Redis cache).
Customer Health Score Model: Gradient Boosting model (XGBoost) trained on 24 months of historical data with 40+ features: login frequency, support tickets, email marketing engagement, payment history, NPS score, usage growth rate. Model updates daily with new data.
Alert & Action System: When a customer's health score drops below the danger threshold, the system automatically sends an alert to the account manager with specific action recommendations: offer a discount, schedule a check-in call, escalate to senior relationship manager.
Real-time Dashboard: React dashboard with customer health heatmap, churn forecast by cohort, and tracking the effectiveness of each intervention.
Results
- 94% accuracy in predicting churn within the next 30 days
- 2,300+ customers retained per quarter (vs. baseline before the system)
- -18% churn rate compared to before deployment
- +$2.1M ARR protected in the first 6 months
- Average alert lead time: 47 days before a customer would actually churn — enough time to intervene
Technology
Python + XGBoost for ML model. Apache Airflow for pipeline scheduling. PostgreSQL as data warehouse. Redis for real-time caching. React + D3.js for dashboard. Salesforce API, Outlook API, social media connectors.