Use case
Most churn is visible before it happens: usage falls, contact stops, a renewal date approaches with no conversation. The work is capturing those signals somewhere a person will see them in time to act.
The visible symptom is rarely the cause. These are the underlying reasons we find most often.
Accounts at risk are identified while there is still time to act, with the signal reaching a named person and a defined response rather than a dashboard.
Context
Retaining an existing customer is materially cheaper than replacing one, and churn compounds: the accounts lost this quarter also take their referrals and their expansion revenue with them.
How we solve it
Six to twelve weeks to instrument and alert. Whether churn falls depends on what happens after the alert, which is an operating decision rather than a technical one.
Look at accounts that already left and find what preceded it. The pattern is usually specific to your business (a drop in a particular activity, a change of contact, a support theme) and it is far more useful than a generic health score.
Usage, support and commercial history in one place. This is the step that makes everything after it possible, and it is where most retention programmes stall.
Route a flagged account to a named owner with the context attached. An alert that lands in a dashboard nobody opens has the same effect as no alert.
Agree what happens when an account is flagged. Without that, alerting produces awareness of churn rather than prevention of it.
Capabilities
How it works
A process before and after automation
Before: five steps, four of them manual. After: the same outcome with one human decision point and a defined exception path for when the system is unsure.
Patterns can be identified from accounts that already left, and they are usually specific to one business rather than universal. That is more useful than a predictive score nobody can interpret, because it tells you what to look at.
Rarely at the start. Most organisations get most of the value from three or four explicit rules built from observed patterns. A model becomes worth considering once those rules are running and the volume justifies refinement.
Most churn is visible before it happens: usage falls, contact stops, a renewal date approaches with no conversation. The work is capturing those signals somewhere a person will see them in time to act.
Retaining an existing customer is materially cheaper than replacing one, and churn compounds: the accounts lost this quarter also take their referrals and their expansion revenue with them.
Cancellations arrive as a surprise to the account team; Renewal conversations start in the last fortnight of a contract; Nobody can say which accounts are currently at risk; and Support knows an account is unhappy and the commercial team does not
Usage and engagement data lives in a system the commercial team does not open; There is no agreed definition of what a declining account looks like; Renewal dates are tracked in a spreadsheet nobody reviews on a schedule; and Support tickets and account health are never joined up
Accounts at risk are identified while there is still time to act, with the signal reaching a named person and a defined response rather than a dashboard.
Six to twelve weeks to instrument and alert. Whether churn falls depends on what happens after the alert, which is an operating decision rather than a technical one.
Connected
If the before state above reads like your operation, the next step is establishing which part of it is actually costing you. Describe it and we will tell you where reduce customer churn would and would not help.
