What ChartMogul Can't Tell You About Churn

ChartMogul, Baremetrics, ProfitWell — these tools are excellent at showing you that churn happened. A customer canceled. MRR dropped. The line went down.

But they can't tell you why. And "why" is the only thing that lets you do something about it.

The single-source blind spot

Billing analytics tools see one thing: payment data. They know when a subscription was created, when it was canceled, and the dollar amount. That's it.

They don't know that the customer's product usage dropped 60% two weeks before cancellation. They don't know that the customer opened 4 support tickets in their last month. They don't know that the customer's company just laid off their entire data team — the people who actually used your product.

Churn is a multi-source event. The billing system records the outcome, but the causes live in your product database, your CRM, your support tool, and sometimes in external signals you're not even tracking.

What a multi-source view reveals

When you connect billing data (Stripe) with product data (your Postgres database) and CRM data (HubSpot), churn patterns become visible that are completely invisible to single-source tools.

Pattern 1: Usage decay precedes cancellation by 2–4 weeks. In our analysis of SaaS companies between $5M and $20M ARR, we consistently find that a 40%+ drop in weekly active usage is the strongest predictor of churn — stronger than NPS scores, support tickets, or contract value. But this signal lives in your product database, not in Stripe.

Pattern 2: Role changes trigger churn. When the champion who bought your product leaves the company (detectable via CRM contact changes or email bounces), the account churns within 90 days approximately 65% of the time. This signal lives in your CRM, not in Stripe.

Pattern 3: Feature adoption is the real retention driver. Customers who adopt 3+ core features in their first 30 days have 4x lower churn than customers who only use one feature. This signal lives in your product analytics, not in Stripe.

Why causal decomposition matters

Knowing that churn increased 15% month-over-month is useful. Knowing that 68% of that increase came from 3 enterprise accounts that all had declining usage and were approaching annual renewal — that's actionable.

Causal decomposition breaks an aggregate metric change into its component causes. Instead of "churn went up," you get "churn went up because of X, Y, and Z, and here's what you can do about each one."

This requires entity resolution (matching the same customer across sources), metric computation (calculating churn from raw subscription data), and anomaly detection (flagging when churn deviates from baseline). No single-source tool can do all three.

The proactive alternative

The traditional analytics workflow is reactive: churn happens, you see it in your dashboard, you investigate, you maybe do something about it. The cycle takes weeks.

The alternative is proactive: identify the signals that precede churn, detect them in real-time across all your data sources, and alert the right person before the cancellation happens.

This is what we're building at Vesh AI. We connect to your existing data sources (no migration required), resolve entities across them, compute metrics from the raw data, and deliver actionable insights to Slack — including early warnings about accounts likely to churn.

The difference between "Sarah from Acme Corp canceled yesterday" and "Sarah from Acme Corp hasn't logged in for 14 days, opened 2 unresolved support tickets, and her annual renewal is in 30 days" is the difference between analytics and intelligence.

What to do about it

If you're currently using a billing analytics tool, don't stop. They're great at what they do — tracking MRR, visualizing trends, benchmarking against peers.

But layer on a tool that connects your product data and CRM data to your billing data. Whether that's Vesh AI, a custom data pipeline, or even a weekly SQL query that joins your tables — the multi-source view will show you things that single-source analytics never can.