Customer Rescue AI — Detect Churn Risk & Improve Retention

Retention rescue brief — illustrative

Evidence: customer-stated possible cancellation linked to unresolved export tickets; supplied usage decline during the issue. Risk hypothesis: unresolved value-blocking problem may lead to voluntary cancellation. This is stronger than an isolated activity signal, but it is not a certain outcome or a quantified probability.

Cause hypothesis: failed export blocks the customer’s intended value. Technical cause and alternative explanations are unknown; inspect tickets and approved troubleshooting evidence before assigning cause.

Priority: prompt human review of the unresolved blocker and an honest recovery plan. Customer impact and urgency are plausible from the stated intent; revenue exposure, renewal timing and intervention cost remain unknown. Preventability depends on feasible resolution.

Proposed playbook: confirm desired export outcome and previous troubleshooting; assign an authorized technical owner; communicate the next verified step and an agreed update time; confirm whether recovery restores value; respect the customer’s cancellation choice. Do not offer unauthorized concessions or imply cancellation is blocked.

Draft response: “I understand the unresolved export issue is affecting whether this subscription works for you. I’d like to confirm what you need from the export and review the two tickets so we can give you a clear next step. We’ll agree an update time based on the technical review. If you choose to cancel, we’ll follow the applicable process.”

Measure: actual blocker resolution, customer-confirmed usefulness, subsequent cancellation or continuation, and recurrence of the export issue. Do not attribute retention to the intervention without appropriate evidence.

Execution status: NOT_EXECUTED. No tickets accessed or updated, message sent, refund or discount issued, or cancellation changed.