Customer Health Score: Composite Metric for Account Risk Assessment,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n
Design a weighted Customer Health Score combining product usage, support interactions, billing history, and engagement signals to predict account risk.
Build a composite health score (0-100) that predicts customer churn risk, identifies expansion opportunities, and prioritizes Customer Success team efforts.
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Prompt objective
Build a composite health score (0-100) that predicts customer churn risk, identifies expansion opportunities, and prioritizes Customer Success team efforts.
Real use case
A B2B SaaS company with 500 accounts has 3 CSMs who can't proactively reach everyone. They need a health score to automatically flag at-risk accounts so CSMs can intervene before customers cancel.
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Prompt
Design a Customer Health Score system for [COMPANY NAME], a [B2B/B2C] company with [NUMBER] customers.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Health Score Components (weighted):**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n1) Product Usage (30%):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Login frequency (days active in last 30)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Feature adoption (% of available features used)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Usage depth (actions per session)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Trend: increasing, stable, or declining over 30 days\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n2) Engagement (20%):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Email open rate\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Event/webinar attendance\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Community/forum participation\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- NPS/CSAT score\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n3) Support (15%):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Number of tickets (too many = bad, zero = also potentially bad)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Ticket severity\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Time to resolution satisfaction\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Escalation history\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n4) Business Relationship (20%):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Contract length remaining\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Payment history (late payments?)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Stakeholder engagement (multiple contacts vs. single point of failure)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Expansion signals (usage above plan limits)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n5) Financial (15%):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Revenue trend (expansion/contraction)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Margin on account\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Days sales outstanding\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Scoring System:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Each component: 0-100 score\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Weighted total: 0-100\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Zones: Green (70-100), Yellow (40-69), Red (0-39)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Implementation:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Data sources for each component\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Automated calculation frequency (daily/weekly)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Dashboard view for CSM team\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Alert rules for zone changes\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Historical health score trend per account\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nInclude SQL or Python code for the calculation.
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