What is churn prediction?
Churn prediction estimates the chance that a customer will cancel or not renew. In consumer businesses it is usually a statistical model over millions of users. In B2B, with fewer and larger accounts, it is more often a scored set of signals reviewed by people who know the account.
Which signals predict churn in B2B accounts?
| Signal | Where it lives | Why it matters |
|---|---|---|
| Usage falling | Product analytics | People stop using what they no longer value |
| Unresolved tickets | Support desk, WhatsApp groups | Repeated asks erode trust |
| Late invoices | Billing | Payment friction often comes before a cancellation |
| Champion left | CRM, email | The person who bought you is gone |
| No recent contact | CRM, calendar | Nobody has heard how they are doing |
| Stopped opening emails | Marketing tools | Attention has moved elsewhere |
How do you build a churn score?
- Pick a few signals you can measure reliably for every account.
- Weight them by how often they preceded churn in your own past renewals.
- Score every account the same way, on a schedule.
- Review the scores against what actually renewed, and adjust the weights.
With enough closed renewals, a statistical model can learn the weights. With fewer, a weighted score that a person reviews is more honest than a model that overfits.
Why do churn scores fail?
Most scores fail on action, not accuracy. The signals sit in different tools owned by different teams, so the account that is clearly at risk on paper has no owner in practice. A score needs an owner, a plan and a date.
How does WiseOrg help?
WiseOrg joins the signals per account across your tools, scans every six hours for at-risk accounts nobody is working on, and opens an initiative with the evidence, a plan and an owner. After each action it re-reads the account's numbers to see whether the risk moved.