SkillsLib.ai

Churn Risk

Surface at-risk customers before they churn using support ticket analysis

4.2(33 reviews)
100+ downloads
Updated Sep 2026
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What You Can Do

You can feed support ticket data, interaction logs, and customer engagement metrics into Claude, which automatically surfaces churn risk signals—declining engagement patterns, sentiment shifts toward frustration, feature adoption gaps, and escalation trajectories. Claude generates risk scores (Critical/High/Medium/Low) with specific evidence and recommends targeted interventions before customers reach the cancellation stage.

Features

Ticket frequency analysis

detects withdrawal patterns and declining support engagement over time

Sentiment trend detection

flags frustration, resignation, or reduced urgency in customer communications

Feature adoption gap identification

surfaces unresolved implementation issues correlated with churn risk

Escalation trajectory mapping

identifies sequences of escalations that historically precede account loss

Risk scoring engine

generates Critical/High/Medium/Low risk ratings backed by specific evidence

Intervention recommendations

suggests targeted retention actions (training, feature walkthroughs, executive outreach) based on risk type

Account health synthesis

aggregates multiple risk signals into a single customer health view for CS prioritization

Example Output

Example 1: High-Risk Account

Account: Acme Corp | Risk Level: HIGH Evidence:

  • 7 support tickets in 30 days (3x normal baseline)
  • Sentiment shift: tickets 1-3 collaborative → tickets 4-7 frustrated tone
  • Core feature adoption: reporting module 0% usage despite contract inclusion
  • Escalations: 2 escalations in last 10 days (no escalations in prior 90 days)

Recommended Intervention: Schedule product training session on reporting module within 7 days + assign CSM for bi-weekly check-ins


Example 2: Critical-Risk Account

Account: TechStart Inc | Risk Level: CRITICAL Evidence:

  • 12 support tickets in 45 days; 4 unresolved as of today
  • Sentiment: "unclear ROI," "not meeting our needs," "considering alternatives"
  • Feature adoption: 2 of 5 purchased modules completely unused
  • Escalation pattern: Support → Manager → Director in 20 days
  • Contract renewal: 60 days away

Recommended Intervention: Executive sponsor outreach + product capabilities assessment + renewal conversation within 48 hours

What's Included

  • SKILL.md: instruction file with churn risk analysis framework
  • Support ticket analysis template: standardized format for feeding ticket data, sentiment, and engagement metrics into Claude
  • Risk scoring rubric: critical/high/medium/low assessment criteria with evidence weighting
  • Intervention playbook: 8-10 targeted retention actions keyed to risk type (feature training, executive engagement, usage reviews, etc.)
  • Health score dashboard template: spreadsheet format to aggregate and track accounts across risk tiers and track intervention outcomes

Who It's For

  • Customer Success Managers — identify accounts to prioritize for proactive retention outreach
  • Support Managers — flag deteriorating customer relationships before they escalate to cancellation
  • Account Executives — surface renewal risk ahead of contract renewal conversations
  • Customer Support Engineers — catch service quality issues correlating with churn risk
  • Customer Retention Leaders — build data-driven intervention playbooks at portfolio scale

Best For

  • Pre-renewal risk assessment — identify which accounts need intervention 30-60 days before renewal
  • Post-escalation triage — determine if escalations signal deeper churn risk or isolated issues
  • Feature adoption audits — connect unused features to engagement and churn patterns
  • Sentiment-driven outreach — detect tone shifts in support interactions that precede account loss
  • Intervention prioritization — rank accounts by churn risk to allocate CS resources efficiently

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