cs-analytics-dashboards

name	cs-analytics-dashboards
description	Build customer success analytics dashboards — NPS, CSAT, CES, customer health scores, churn prediction models, expansion propensity, support volume trends, and CS team performance. Use when designing CS metrics, building health score models, setting up CS dashboards, analyzing churn patterns, or measuring CS team effectiveness. Triggers on: "CS analytics", "health score", "churn prediction", "NPS dashboard", "CS metrics".
license	MIT
compatibility	Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose
metadata	{"version":"1.0.0","author":"LeadMagic","category":"customer-success","tags":["cs-analytics","health-score","nps","csat","churn-prediction","customer-success-metrics","dashboards"],"related_skills":["cs-playbooks","sla-management","support-tool-stack","churn-prevention","expansion-selling","gtm-metrics"],"frameworks":["Gainsight — Customer Health Score Framework","Totango — Customer Success Maturity Model","Bain & Company — NPS (Net Promoter System, Fred Reichheld)","CustomerGauge — Account Experience (B2B NPS)","David Skok — SaaS Churn Analysis"]}

CS Analytics Dashboards

Overview

Most CS teams operate on intuition — "I feel like this customer might churn." The mistake: relying on gut instead of a health score that flags at-risk accounts 60 days before cancellation. This skill covers the complete CS analytics stack: health score models, NPS/CSAT/CES programs, churn prediction, expansion propensity, support analytics, and CS team performance dashboards. Every metric mapped to an action.

Authoritative Foundations

• Gainsight — Customer Health Score Framework — Customer Health Score Framework
• Totango — Customer Success Maturity Model — Customer Success Maturity Model
• Bain & Company — NPS (Net Promoter System, Fred Reichheld) — NPS (Net Promoter System, Fred Reichheld)
• CustomerGauge — Account Experience (B2B NPS) — Account Experience (B2B NPS)
• David Skok — SaaS Churn Analysis — SaaS metrics — CAC payback, LTV/CAC, unit economics by stage.

When to Use

Trigger phrases: "build CS dashboard", "customer health score", "churn prediction model", "NPS survey design", "CSAT dashboard", "CS team metrics", "expansion propensity score", "customer success analytics", "health score design", "predict churn", "CS KPIs"

The CS Analytics Stack

Layer 1: Customer Sentiment (NPS, CSAT, CES)

NPS (Net Promoter Score):

• Question: "How likely are you to recommend [Product] to a colleague?"
• Scale: 0-10. Promoters (9-10), Passives (7-8), Detractors (0-6)
• NPS = % Promoters - % Detractors
• Benchmark: SaaS average NPS is ~30-40. Above 50 is excellent. Above 70 is world-class.
• Cadence: Every 6 months for B2B SaaS (quarterly creates survey fatigue)
• Follow-up: "What's the primary reason for your score?" (open text = gold)

CSAT (Customer Satisfaction):

• Question: "How satisfied are you with [specific interaction]?"
• Scale: 1-5 (stars or emoji)
• Target: 4.0+ average. Below 3.5 = systemic issue.
• Trigger: Post-ticket resolution, post-onboarding, post-QBR
• Follow-up: Scores < 3 auto-escalate to manager for personal outreach within 24 hours

CES (Customer Effort Score):

• Question: "How easy was it to [complete task]?"
• Scale: 1-7 (1=very difficult, 7=very easy)
• Target: 5.5+ average
• High effort = high churn risk (Gartner: 96% of high-effort customers churn)
• Best use case: Post-onboarding, post-setup, post-integration

Layer 2: Customer Health Score

The 5-dimension health score model:

Dimension	Weight	Metrics	Data Source
Product Usage	35%	Login frequency, feature adoption, depth of use	Product analytics (Amplitude/Mixpanel/Segment)
Engagement	20%	Support tickets, NPS responses, QBR attendance, email opens	Intercom/Zendesk, NPS tool, email
Financial	20%	Payment history, plan tier, expansion/renewal status, credit risk	Stripe/Billing, CRM
Relationship	15%	Executive contact changes, multi-thread depth, champion health	CRM, LinkedIn, email
Outcomes	10%	Value delivered vs expected, ROI realization, goal attainment	QBR notes, surveys

Scoring formula:

Health Score = (Product Usage × 0.35) + (Engagement × 0.20) + (Financial × 0.20)
             + (Relationship × 0.15) + (Outcomes × 0.10)

Each dimension scored 0-100:
- 85-100: Green (healthy — expand, reference, case study)
- 70-84: Yellow (watch — engagement play, additional training)
- 50-69: Orange (at-risk — intervention required, executive outreach)
- 0-49: Red (critical — immediate escalation, save play)

Specific dimension scoring examples:

Product Usage (0-100):

• 100: Daily active users, 80%+ feature adoption, power user behavior
• 75: Weekly active, 50%+ adoption, core features used
• 50: Monthly active, 25% adoption, surface-level usage
• 25: Erratic login, <10% adoption, single feature only
• 0: No logins in 30+ days (abandoned)

Engagement (0-100):

• 100: Attends QBRs, responds to outreach, submitted NPS, opens emails
• 75: Responds to CSM, attends some QBRs, occasional survey response
• 50: Responds only when they need something
• 25: Unresponsive to CSM outreach, ignores surveys
• 0: Zero engagement in 90+ days (ghost)

Layer 3: Churn Prediction

Early warning signals — ranked by predictive power:

#	Signal	Lead Time	Action
1	Champion leaves company (job change)	60-90 days	Immediately engage replacement, rebuild champion
2	Usage drops 30%+ month-over-month	30-60 days	Proactive outreach, training, "value realization" session
3	Key user stops logging in	60-90 days	"We noticed you haven't logged in — is everything OK?"
4	Support tickets become complaints	30-60 days	Manager review, executive outreach if pattern
5	Payment becomes late	0-30 days	Immediate billing + CSM outreach
6	NPS score drops 20+ points survey-over-survey	30-90 days	Detractor follow-up within 24 hours
7	Champion stops responding to emails	30-60 days	Escalate to executive contact, try alternate channels
8	Competitor evaluation — customer asks about comparison	60-90 days	Competitive battlecard, executive alignment, value proof

Simple churn prediction model (for early stage):

Churn Risk Score = 0-100

Risk Factors (add points):
+30: Champion churned (job change detected)
+25: Usage dropped >30% MoM
+20: NPS = Detractor (0-6)
+15: Payment late >15 days
+10: Key user inactive >30 days
+10: Customer unresponsive >30 days
+5: Support ticket negative sentiment

Risk Bands:
- 0-20: Low risk — business as usual
- 21-40: Moderate — CSM engages with training/value play
- 41-60: High — manager + CSM intervention, executive outreach
- 61-100: Critical — VP CS + CEO outreach, save play

Layer 4: Expansion Propensity

Signals that a customer is ready to expand:

Signal	Propensity	Action
Usage approaching plan limits	High	Proactive outreach: "You're about to outgrow your plan. Here's what's next."
Multiple teams/departments using product	High	Enterprise upsell or seat expansion
Champion requests feature in higher tier	Very High	"That feature is available in Growth. Let's walk through the upgrade."
Customer asks about API/webhooks	Very High	Platform/Enterprise upsell with integration support
QBR reveals expanding use case	High	Multi-product or seat expansion
NPS Promoter + healthy usage	Medium	Case study, reference program, then expansion conversation

Expansion Score (simplified):

Expansion Score = 
  (Usage approaching limit: +30) +
  (Multi-team adoption: +25) +
  (Requested higher-tier feature: +25) +
  (NPS Promoter: +10) +
  (QBR attendance: +10)

Score:
- 70-100: Expansion playbook now
- 40-69: Nurture — additional training, showcase advanced features
- 0-39: Focus on adoption before expansion

Layer 5: CS Team Performance

Metrics to evaluate individual CSMs:

Metric	Good	Warning	Red Flag
NRR (book of business)	>110%	100-110%	<100%
Logo Churn Rate	<1%/mo	1-2%/mo	>2%/mo
Health Score (avg)	>80	70-80	<70
QBR Completion Rate	>90%	75-90%	<75%
Expansion Revenue	>10% of book	5-10%	<5%
NPS (book of business)	>50	30-50	<30
Time to First Value (new accounts)	<14 days	14-30 days	>30 days

CS Team Dashboard (weekly review):

CS TEAM DASHBOARD — Week of [date]

BOOK OF BUSINESS HEALTH:
| Metric | Value | Trend |
|---|---|---|
| Total Accounts | X | |
| At-Risk (Orange+Red) | X (Y%) | |
| Healthy (Green) | X (Y%) | |
| NRR (trailing 3mo) | X% | |
| Logo Churn (monthly) | X% | |

TEAM PERFORMANCE:
| CSM | Accounts | NPS | NRR | Churn | Health >80 | QBRs Done |
|---|---|---|---|---|---|
| [name] | X | X | X% | X% | X% | X/Y |
| ... |

AT-RISK ACCOUNTS (Red/Orange):
| Account | Score | Primary Risk | CSM | Last Action | Next Step |
|---|---|---|---|---|---|
| [name] | 38 | Champion left | [CSM] | [date] | Exec outreach |
| ... |

EXPANSION OPPORTUNITIES:
| Account | Expansion Score | Opportunity | CSM | Next Step |
|---|---|---|---|---|
| [name] | 85 | Seat expansion | [CSM] | Send proposal |
| ... |

Output Format

CS ANALYTICS SPEC — [Company]

SURVEY PROGRAM:
- NPS: [question, scale, cadence, follow-up]
- CSAT: [question, trigger, scale, escalation]
- CES: [question, trigger, target]

HEALTH SCORE MODEL:
[5-dimension table with weights, metrics, data sources]
Scoring: 0-100 with 4 bands (Green/Yellow/Orange/Red)

CHURN PREDICTION:
- Model: [simple points-based / ML]
- Signals: [ranked list with lead times and actions]
- Alert cadence: [daily / weekly]

EXPANSION PROPENSITY:
- Signals: [list]
- Model: [scoring]

DASHBOARD:
- CS Team Dashboard: [weekly review, owner]
- Book of Business Health: [metrics and targets]
- At-Risk Accounts: [review cadence, playbook triggers]

Implementation Checklist

• Health score has 5 dimensions with explicit weights (sums to 100%)
• Each health dimension has 3+ measurable sub-metrics
• Health bands have specific actions (not just "check on customer")
• NPS survey cadence is 6-month (not monthly — avoids survey fatigue)
• CSAT surveys triggered by specific events, not randomly
• Scores < 3 on CSAT auto-escalated to manager within 24 hours
• Churn prediction model generates weekly at-risk list
• Champion churn (job change) detection is automated
• CS team metrics reviewed weekly, not just quarterly
• Expansion propensity triggers specific playbook, not just "upsell"

Quality Check

Before delivering, verify:

• Output matches the user's stated request
• Named frameworks or sources are reflected in the recommendation
• The deliverable is specific enough for an agent to execute
• Any assumptions, risks, or dependencies are explicit
• No unsupported claims, invented facts, or private/internal references are included

Common Pitfalls

1. Health score without action. "Customer is at 38" without "here's the specific 3-step save play" is useless. Fix: Every health band maps to a specific playbook. Green → expansion. Yellow → engagement. Orange → intervention. Red → save.

2. Too many survey questions. NPS + CSAT + CES + onboarding survey + feature survey + quarterly survey = survey fatigue and 5% response rates. Fix: NPS twice/year. CSAT post-interaction only. One onboarding survey. That's it.

3. Composite health score hiding problems. Average 75 looks healthy but masks that Usage is 100 and Financial is 50 (customer loves the product but is about to go bankrupt). Fix: Review dimension scores individually, not just the composite.

4. Detractors without follow-up. NPS Detractor without personal outreach within 24 hours = you don't actually care about feedback. Fix: Auto-notify CSM + manager on Detractor. Template for follow-up call. Track close rate (Detractor → Promoter conversion).

5. Measuring activity instead of outcomes. "# of QBRs completed" is an activity metric. "% of QBRs that uncovered expansion opportunities" is an outcome metric. Fix: Every CS metric should tie to revenue or retention.

6. Champion churn undetected. Your champion leaves the company and you find out 3 months later when the contract doesn't renew. Fix: Automated job change monitoring (LinkedIn alerts, LeadMagic Job Change, manual LinkedIn check quarterly).

Execution Artifacts

• references/framework-notes.md — Named frameworks and reference tables
• templates/output-template.md — Deliverable shell for agent output
• scripts/check-output.py — Lightweight deliverable validator Canonical lifecycle (repo root): references/activation-playbook.md · references/lifecycle-metrics-by-stage.md (Activation, Engagement, Retention) · skills/analytics/gtm-metrics/templates/lifecycle-monitoring-dashboard.md

Related Skills

• cs-playbooks — Playbooks triggered by health score bands
• sla-management — SLA design, escalation paths
• support-tool-stack — Platform analytics and reporting
• churn-prevention — Early warning signals, intervention plays
• expansion-selling — Propensity models, expansion plays
• gtm-metrics — Complete SaaS metrics stack