AI Customer Churn Prediction

Overview

A churn prediction and customer-save system for B2B SaaS and subscription businesses. The model ingests product usage data, support ticket patterns, billing history, NPS/CSAT signals, and CRM contact data, then predicts churn probability 30 days out with calibrated confidence. Each at-risk customer is tagged with the specific signal that flagged them (login decline, champion offboarded, support volume spike, payment failure, feature-usage drop) and matched to a recommended save play from your playbook (executive sync, pricing review, additional training, free-month offer). CS leadership gets a weekly priority list. Reps get the same data inside their CRM workflow.

The Challenge

Churn modeling is harder than it sounds because the signal-to-noise ratio is low — most customers who look 'at-risk' by any single metric will renew anyway, and some who look healthy will silently leave. The model has to combine many signals weighted by your customer base's actual behavior, not generic benchmarks. It needs to be explainable so the CS team can act on it. It has to adapt to seasonal patterns and product changes without manual retraining. And the recommended save plays need to be your playbook, in your voice, mapped to actual budget reality.

Our Approach

We train a gradient-boosted churn model on 18–24 months of your historical customer data — ideally with cancellation reasons tagged. The model is calibrated so probability scores are interpretable as actual risk percentages, not just relative rankings. SHAP values surface the top 3 drivers per at-risk customer in plain English. Save plays come from your existing playbook (we workshop them with your CS leadership in week 1); each play has a defined trigger, owner, and tracked outcome. Integration is native to Gainsight, Vitally, Catalyst, or directly into Salesforce / HubSpot if you don't run a CS tool. Monthly retraining is automated; quarterly review with your team keeps the model aligned to changing business reality.

Key Features

• 30-day forward churn probability with calibrated confidence
• Top-3 drivers per at-risk customer (SHAP-based explanations)
• Save-play engine that recommends specific actions, not generic alerts
• Native integration: Gainsight, Vitally, Catalyst, Salesforce, HubSpot
• Per-segment models (enterprise, mid-market, SMB) for accuracy
• Backtested performance reporting vs. rules-based baseline
• Automated monthly retraining, quarterly model review
• Per-rep dashboard inside the CRM — no separate UI to learn

Results

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