Feature · Churn prediction
Most CS teams find out a customer is leaving the day they ask to cancel. Exeechain's churn prediction surfaces the same risk thirty days earlier — with the three biggest drivers per customer explained in plain English, refreshed daily.
What it is
Exeechain's churn prediction is a machine-learning model that scores every customer 0–100 every day. The score answers a single question: how likely is this customer to churn in the next thirty days? A score of 78 doesn't mean anything by itself. The value is in the three drivers attached to it: login down 62% over fourteen days, NPS dropped from 9 to 6, primary champion moved to a different role at the same company.Those are the three things to talk about in tomorrow morning's save call.
Unlike rules-based scoring (ChurnZero, configurable Vitally, custom Planhat formulas), there's nothing to author or maintain. The model retrains automatically as your customer base evolves and as you connect more data sources. Most teams see the score get meaningfully sharper in the first two weeks just from feeding it support and CRM data on top of the initial Stripe + tracking snippet baseline.
How it works
Step 01
Connect Stripe (for MRR + customer list) and drop in a JavaScript tracking snippet for product usage. Optionally connect HubSpot or Salesforce, Intercom or Zendesk, and Mixpanel — each one lifts model accuracy 20-30%.
Step 02
The model ingests billing patterns, login frequency, feature adoption, support ticket sentiment, NPS history, champion role changes, billing failures, and renewal proximity. First scores produce within 15 minutes; full training stabilizes in 24 hours.
Step 03
Every customer gets a fresh 0-100 score every morning. Each score lists the three biggest drivers in plain English, plus the suggested action — save email draft, executive escalation, downgrade conversation, or expansion outreach.
Why it matters for NRR
Net revenue retention is the single most leveraged metric in SaaS. A 5-point NRR improvement compounds into a 70%+ valuation lift over five years.
Most save outreach happens after the customer has already mentally decided to leave. Predicting churn 30 days early shifts the conversation from damage control to relationship repair.
Customers caught on the 30-day-early curve respond to outreach at 2-3x the rate of customers caught at cancellation. The earlier the contact, the warmer the conversation.
Renewal cohorts weighted by current health scores produce defensible quarterly forecasts. The number you take to your board meeting is the number that materializes.
Live in fifteen minutes. From $299/mo. Daily AI churn scoring with the top three drivers per customer.