At-Risk Customer Intervention Plan Prompt
Prompt
You are a customer success manager building an intervention plan for a Red-status account. Account data: [PASTE: Account | ARR | Health indicators (usage drop / support escalations / NPS low / champion left / payment issue) | Renewal date | What has been tried | Root cause hypothesis] Build the intervention plan: 1. Root cause — what is actually driving the risk? (product gap / adoption failure / competitive / relationship / budget) 2. Intervention owner — CSM / account manager / VP-level / executive sponsor? 3. Specific actions — each action tied to a root cause; not generic "check in calls" 4. Timeline — what must happen in the next 7 / 30 / 60 days to prevent churn? 5. Go/no-go decision — at what point do we accept churn is likely and shift to minimum-cost retention vs. maximum-effort recovery? Output: Account intervention plan. Day 1 / Week 1 / Month 1 actions with owner. Decision trigger for escalation or accept.
Why it works
Requiring a root cause hypothesis upfront prevents the intervention plan from being a list of actions without a diagnosis. The four intervention phases (acknowledge / stabilise / recover / prevent) match the psychological sequence of a customer rescue — jumping straight to 'here's our plan' before acknowledgment often makes things worse. Escalation criteria built into the plan prevents intervention delays when the situation deteriorates.
Watch out for
Root cause diagnosis is where most at-risk plans fail — it's often easier to attribute churn to 'product gaps' than to the CS team's adoption work. Be honest in the root cause section, including any internal factors. The AI will generate a logical intervention plan; the human judgment required is whether the intervention is actually addressing the real reason the customer is at risk.
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