What signals predict churn?

The strongest predictors are declining usage, a drop in key-feature adoption, unresolved support issues, and failed payments. Stated cancellation reasons matter too, but they often mask the real trigger — which is why combining behavioural and stated data gives the clearest picture.

Can AI tell me why customers churn?

AI can cluster cancellation reasons, correlate them with usage patterns, and surface likely root causes from your data — but it diagnoses from what you supply. The prompt is built to separate the stated reason from the probable underlying cause.

How should I act on a churn analysis?

Prioritise interventions by the size of the cohort they affect and how addressable the cause is. The prompt ends with a ranked action list so you fix the churn that is both common and fixable first.

Does my churn data leave my browser?

No. The builder assembles the prompt locally and outputs only text. You paste the data into your own LLM, so it stays under your control.

Churn Analysis Prompt Builder

Churn analysis prompt builder

Stopping churn starts with diagnosing it honestly, and the stated reason on an exit survey is rarely the real one. “Too expensive” often means “I never got value,” and “missing a feature” often means “I never found it.” This builder turns your cancellation data into an LLM prompt that separates stated reasons from probable root causes, segments the damage, and proposes interventions you can actually run.

How it works

You pick the data type — cancellation reasons, behavioural usage signals, exit-survey free text, or a combination — set the time period, and name the segments you care about, such as plan tier or signup cohort. The builder assembles a prompt that instructs the model to cluster reasons, correlate them with behaviour where available, distinguish stated reasons from likely root causes, quantify each driver, and propose a prioritised list of retention interventions tied to the most common, most addressable causes. It requires the analysis to stay grounded in your data rather than generic churn advice.

Tips and notes

Combine stated and behavioural data. Reasons alone mislead; pairing them with usage signals reveals the true trigger. The prompt asks for both.
Segment before you act. Churn in your free tier and your enterprise tier have different cures — the prompt diagnoses per segment.
Prioritise by reach and fixability. Chase the churn that is both common and addressable first; the prompt ranks interventions on exactly that.
Treat stated reasons with suspicion. The prompt explicitly looks past the survey answer for the underlying cause.