SaaS Churn Predictor v1.0: Stripe-Native Churn Scoring for Founders

Every churned customer was a signal you didn’t catch in time. Login frequency dropped three weeks before. Payment failed twice last month. Support ticket volume doubled. You saw each event in isolation. By the time the cancellation hit, it was already over.

That’s the problem SaaS Churn Predictor solves. It connects to Stripe, watches the behavioral and billing signals that precede churn, and surfaces a ranked list of at-risk customers before they leave. Today we’re shipping v1.0 — 597 tests, 135 test suites, and a feature set built for founders who need churn visibility without a CS platform.

The Core Idea: Prediction, Not Management

Most churn tools are customer success platforms. Gainsight ($5,000+/mo), ChurnZero ($1,000+/mo), Vitally ($1,000+/mo) — they manage CSM workflows, run playbooks, and track lifecycle journeys. They require a CS team, a CRM integration, and weeks of implementation.

SaaS Churn Predictor does one thing: tell you who is about to leave, why, and what to do about it.

No CRM required. No CSM seat licenses. No multi-week implementation. Connect Stripe, get scores, start acting.

How It Works

1. Connect Stripe

Paste a restricted Stripe key. Customers, subscriptions, invoices, and payments sync automatically. Setup takes under a minute. No CSV uploads, no data pipeline configuration, no warehouse sync.

2. Nightly Scoring Run

The Risk Scoring Engine v2 analyzes four signal categories for every customer:

• Payment history — failed charges, late payments, dispute frequency
• Engagement trends — login frequency changes, feature usage narrowing, session duration shifts
• Support interactions — ticket volume spikes, escalation patterns, sentiment changes
• Lifecycle stage — subscription age, plan change velocity, trial-to-paid conversion lag

Each customer receives a 0–100 risk score with tier classification: Low (safe), Medium (watch), High (act now). Every score comes with a plain-English explanation of the top contributing factors.

3. Act on the Signal

The dashboard shows MRR-weighted risk — a $2K/mo account at risk surfaces above a $29/mo account. Each at-risk customer gets a specific recommended action: reach out, offer a plan change, check payment status, or trigger a support follow-up.

The intervention pipeline tracks every save attempt: Recommended → Contacted → Responded → Saved/Lost. Over time, you learn which intervention types have the highest save rate.

Voluntary vs. Involuntary Churn: Two Problems, Two Playbooks

Not all churn is the same. A customer who cancels because they outgrew your product is different from a customer whose card expired and never updated it. SaaS Churn Predictor classifies each churn signal as voluntary (cancellations, downgrades — fix the product experience) or involuntary (failed payments, expired cards — fix the billing flow).

This distinction matters. Industry data shows 20–40% of SaaS churn is involuntary. That’s revenue leaking out through payment failures, not through product dissatisfaction. If you can’t tell the difference, you waste time discounting customers who just need their card updated.

The ROI Math

Here’s the calculation that matters:

• A SaaS business with $100K MRR and 5% monthly churn loses $5,000 in MRR per month — $60K per year.
• Reduce churn from 5% to 4% and that LTV grows from $1,000 to $1,250 per customer at $50 ARPU.
• Across 500 customers: +$125,000 in lifetime value from 1 point of churn reduction.

At $29/mo for the Starter plan, saving one customer on a $99/mo plan pays for the tool for three months. The math is simple. The hard part is knowing who to save. That’s what the prediction engine does.

What’s in v1.0

Security & Reliability

v1.0 ships with 597 tests across 135 suites. Security hardening includes:

• Webhook event type allowlist + HMAC signing on outbound webhooks
• CSRF null-origin bypass removal, hardened health POST endpoint
• CSP headers, request timeouts, webhook body size limit (1MB)
• CodeQL analysis and dependency review in CI
• js-cookie pinned to 3.0.7 (CVE override for GHSA-qjx8-664m-686j)
• Upstash Redis rate limiting with in-memory fallback
• Zod validation on 9 schemas across 14 API routes

Pricing

Every plan includes a free tier for testing. Connect Stripe, see your first scores, then decide.

When to Use SCP vs. a CS Platform

We wrote detailed comparisons with Gainsight, ChurnZero, Vitally, Pecan AI, and others. The short version:

• Use SCP if you are a founder or small team who needs to know who is about to leave — without a CS platform, CRM integration, or multi-week implementation.
• Use a CS platform if you have a dedicated CS team that manages accounts in Salesforce and needs playbooks, journey orchestration, and a system of record.
• Use both if you’re at scale. SCP provides the prediction signal. The CS platform manages the relationship workflow. They’re complementary, not competitive.

The prediction layer comes first. You need to know who is at risk before you can build a system around saving them.

What’s Next

v1.1 roadmap priorities, based on early feedback:

• Custom scoring weights — let teams adjust signal importance based on their churn patterns (Enterprise)
• Scenario modeling — simulate the revenue impact of reducing churn by X% before committing resources
• CRM integrations — HubSpot and Salesforce sync for teams that want prediction inside their existing workflow
• Intervention templates — pre-built outreach sequences based on churn type and customer segment

SaaS Churn Predictor is live at saas-churn-predictor.vercel.app. Connect Stripe, get your first risk scores tonight.

View pricing →  ·  GitHub repo →