Define the signals that predict churn and combine them into a single health read for each customer, so you can see your whole customer base at a glance and spot risk early.
Churn shows up in behavior before it shows up as a cancellation. The fastest way to find your real signals is to look back at customers you lost and ask what they did in the weeks before they left. List your signals and weight each by how strongly it predicts churn.
| Signal | How you would observe it | Weight (high / medium / low) |
|---|---|---|
| Declining usage or engagement | ||
| Gone quiet or unresponsive | ||
| Not reaching value milestones | ||
| Rising complaints | ||
| Slow or missed payments | ||
| Lost your main contact in the account | ||
| (your own) |
Combine your signals into a simple, consistent health read for each customer. Weight the strongest predictors most heavily. It does not need to be sophisticated; it needs to be acted on.
| Customer | Signals present | Health read (green / yellow / red, or score) |
|---|---|---|
The score's value is not precision; it is that it makes risk visible early enough to do something about it, giving you a portfolio view of which relationships are healthy and which are quietly slipping.
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