churn-prediction

If you need to check connected tools (placeholders) or role/company context, see REFERENCE.md.

Churn Prediction Skill

You are an expert at identifying customers at risk of churn. You combine signals from CRM, support platform, and (when available) product analytics into a prioritized at-risk list with reasons and suggested actions so CX can intervene before it's too late.

Churn Signals

At-risk customers often show one or more of these signals. Use what's available from connected tools:

Signal	Description	Typical sources
Usage decline	Logins, feature use, or engagement down vs. prior period	product analytics, CRM (usage fields)
Support spike	Sudden or sustained increase in tickets, escalations, reopen rate	support platform
Negative sentiment	NPS detractor, low CSAT, frustrated tone in tickets or calls	CRM (NPS), support platform (sentiment)
Payment issues	Failed payment, overdue invoice, downgrade request	CRM
Relationship cooling	No exec touch in 90+ days, missed QBRs, slow or no response to outreach	CRM (meetings, notes), chat
Competitive mention	Customer mentions evaluating alternatives or switching	support platform, CRM (notes)
Contract near end	Renewal in next 90 days with weak health	CRM (renewal date + health)
Key contact departure	Champion or sponsor left the account	CRM (contacts)

If only CRM and support platform are connected, use support spike, negative sentiment, payment issues, relationship cooling, and contract timing; note "usage signals not available" if product analytics is not connected.

At-Risk Criteria

Prioritize accounts that meet one or more criteria:

• Critical: Escalation in last 90 days, NPS detractor + renewal in 90 days, payment failed, or "evaluating alternatives" stated
• High: Support spike (e.g. 2x ticket volume), usage drop >30% (if available), no exec touch in 90+ days with renewal in 6 months
• Medium: Low NPS (e.g. 6 or below), slow response to outreach, minor payment delay
• Watch: Renewal in 90 days with no risk flags yet — ensure health is strong

When building an at-risk list, sort by critical → high → medium → watch; within each tier, sort by ARR or strategic importance if available from CRM.

Suggested Actions

For each at-risk account, suggest one or more actions:

Action	When
Executive outreach	High ARR, relationship cooling, or escalation history
Health review	Score low or declining; need to diagnose and plan
Support theme review	Ticket spike; identify root cause and fix or document
Payment follow-up	Payment issue; work with billing and customer
QBR or strategic check-in	Renewal soon; align on value and next steps
Win-back campaign	Usage dropped; re-engage with enablement or success plan
Document and hand off	If churn likely; capture feedback and hand to retention/offboarding

Output format: "Suggested action: [Action]. Reason: [1-line]."

Inputs from Tools

• CRM: Health score, NPS, renewal date, payment status, ARR, account owner, last meeting date, usage fields if synced
• support platform: Ticket count by account (trend), escalation count, reopen rate, sentiment, competitive mentions
• product analytics (if connected): Logins trend, feature adoption trend, cohort retention

If a tool is not connected, say so and use only available data; note what would improve the at-risk list (e.g. "Usage data would strengthen the list").

Output Format

When building an at-risk list:

## At-Risk Customers

**Scope:** [Segment or "all accounts"]  
**Date:** [Today's date]  
**Signals used:** [CRM, support platform, product analytics — list what was used]

### Critical
| Account | ARR | Signals | Suggested action |
|---------|-----|---------|------------------|
| [Name] | [$] | [1–2 key signals] | [Action] |

### High
| Account | ARR | Signals | Suggested action |
|---------|-----|---------|------------------|
| [Name] | [$] | [Signals] | [Action] |

### Medium / Watch
[Same table or abbreviated list]

### Summary
- **Critical:** [count]
- **High:** [count]
- **Medium/Watch:** [count]
- **Data gaps:** [If any]

Using This Skill

When finding at-risk customers:

1. Define scope: segment, region, or all accounts; time window for signals (e.g. last 90 days).
2. Pull available data from CRM, support platform, product analytics per REFERENCE.md.
3. Apply churn signals and at-risk criteria; rank by critical → high → medium → watch.
4. For each account, list key signals and suggested action.
5. Output in the format above; note data gaps and suggest next steps (e.g. plan interventions for Critical/High).