ElasticFlow
AI SkillScore CustomersCustomer Successby Alireza Rezvaniv1.0.0

When churn is creeping up and renewal season is here, /customer-success-manager scores your portfolio and surfaces at-risk accounts before they leave.

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Score customer health, predict churn, find expansion plays

  • Health scoring across 4 dimensions: usage (30%), engagement (25%), support (20%), relationship (25%)
  • Risk tiers: Critical (executive escalation), High (CSM intervention), Medium (proactive outreach), Low (monitor)
  • Expansion scoring: upsell, cross-sell, seat expansion with revenue estimates
  • Segment-aware benchmarks for Enterprise, Mid-Market, and SMB — calibrated thresholds per segment
  • QBR-ready output with prioritized accounts, intervention playbooks, and revenue impact

Who this is for

Account Manager / CSM

Score 50+ customer accounts before QBR and surface at-risk accounts before they churn

See skills for this role
VP Sales

See expansion pipeline by account with revenue estimates for the board deck

See skills for this role
RevOps

Standardize churn risk scoring across the portfolio with weighted dimensions

See skills for this role

What it does

QBR prep across 50+ accounts in one afternoon

Your VP wants account scores for the QBR tomorrow morning. /customer-success-manager runs the health scorer on your portfolio JSON, classifies every account Green/Yellow/Red, and gives you prioritized intervention recommendations — in 10 minutes.

Renewal season risk assessment 90 days out

Q4 renewals start in 90 days and you need to know which accounts are at risk. /customer-success-manager runs churn analyzer with 5 signal categories (usage decline, engagement drop, support issues, relationship signals, commercial factors) and surfaces the Critical and High tier.

Board deck needs the expansion pipeline

VP Sales asks for expansion revenue forecast for the board. /customer-success-manager scores upsell, cross-sell, and seat expansion across the portfolio — with revenue estimates per account based on contract gaps and product adoption.

You inherited a portfolio with no scoring

New CSM, 80 accounts, no health scores anywhere. /customer-success-manager builds your baseline in one pass — segment-aware benchmarks for Enterprise/Mid-Market/SMB so you know which accounts deserve weekly attention vs quarterly check-ins.

How it works

1

Export customer data: usage, engagement, support, and relationship signals as JSON

2

Run health_score_calculator on portfolio — get Green / Yellow / Red classification per account

3

Run churn_risk_analyzer — get Critical / High / Medium / Low risk tiers with intervention playbooks

4

Run expansion_opportunity_scorer — get upsell, cross-sell, and seat expansion ranked by revenue

5

Get a QBR-ready report: prioritized accounts, recommended actions, and pipeline by tier

Example

Your portfolio (50 accounts uploaded)
50 accounts, $4.2M ARR
Segments: Enterprise (12), Mid-Market (22), SMB (16)
Q2 renewals: 18 accounts, $1.6M ARR
Last quarter NRR: 108%
10 minutes later
Health Summary
Green:   28 accounts (56%) — $2.4M ARR
Yellow:  19 accounts (38%) — $1.5M ARR
Red:      3 accounts (6%) — $300K ARR
Critical (3 accounts, $300K ARR)
Acme Corp     $180K  Usage -40%, no exec sponsor, NPS dropped 8→4
Beta Inc       $80K  Support tickets +200% Q3, escalations to engineering weekly
Charlie LLC    $40K  Champion left August, no replacement engaged
Expansion Pipeline ($420K)
8 accounts ready for upgrade conversation:
NorthStar    +$60K seat expansion (utilizing 95%+ seats)
DataFlow     +$45K cross-sell to Analytics module
SummitTech   +$80K plan tier upgrade (using power features)
+ 5 more
Recommendations
→ Critical: exec escalation this week (Acme, Beta, Charlie) — VP-level outreach required
→ Renewal risk: 4 Yellow accounts in Q2 renewals — schedule QBRs by EOM
→ Expansion: NorthStar ready for upgrade conversation NOW — strike while usage is hot

Metrics this improves

Deal Velocity
Expansion pipeline by tier
Customer Success
Churn Rate
-20-30% via early intervention
Customer Success
Save Rate
+40% on Critical tier
Customer Success
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Customer Success Manager

Production-grade customer success analytics with multi-dimensional health scoring, churn risk prediction, and expansion opportunity identification. Three Python CLI tools provide deterministic, repeatable analysis using standard library only -- no external dependencies, no API calls, no ML models.

Table of Contents

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data.

Health Score Calculator

Required fields per customer object: customer_id, name, segment, arr, and nested objects usage (login_frequency, feature_adoption, dau_mau_ratio), engagement (support_ticket_volume, meeting_attendance, nps_score, csat_score), support (open_tickets, escalation_rate, avg_resolution_hours), relationship (executive_sponsor_engagement, multi_threading_depth, renewal_sentiment), and previous_period scores for trend analysis.

Churn Risk Analyzer

Required fields per customer object: customer_id, name, segment, arr, contract_end_date, and nested objects usage_decline, engagement_drop, support_issues, relationship_signals, and commercial_factors.

Expansion Opportunity Scorer

Required fields per customer object: customer_id, name, segment, arr, and nested objects contract (licensed_seats, active_seats, plan_tier, available_tiers), product_usage (per-module adoption flags and usage percentages), and departments (current and potential).

Output Formats

All scripts support two output formats via the --format flag:

  • text (default): Human-readable formatted output for terminal viewing
  • json: Machine-readable JSON output for integrations and pipelines

How to Use

Quick Start

# Health scoring
python scripts/health_score_calculator.py assets/sample_customer_data.json
python scripts/health_score_calculator.py assets/sample_customer_data.json --format json

# Churn risk analysis
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json

# Expansion opportunity scoring
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json

Workflow Integration

# 1. Score customer health across portfolio
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json
# Verify: confirm health_results.json contains the expected number of customer records before continuing

# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json
# Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer

# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json
# Verify: confirm expansion_results.json lists opportunities ranked by priority

# 4. Prepare QBR using templates
# Reference: assets/qbr_template.md

Error handling: If a script exits with an error, check that:

  • The input JSON matches the required schema for that script (see Input Requirements above)
  • All required fields are present and correctly typed
  • Python 3.7+ is being used (python --version)
  • Output files from prior steps are non-empty before piping into subsequent steps

Scripts

1. health_score_calculator.py

Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

Dimensions and Weights:

DimensionWeightMetrics
Usage30%Login frequency, feature adoption, DAU/MAU ratio
Engagement25%Support ticket volume, meeting attendance, NPS/CSAT
Support20%Open tickets, escalation rate, avg resolution time
Relationship25%Executive sponsor engagement, multi-threading depth, renewal sentiment

Classification:

  • Green (75-100): Healthy -- customer achieving value
  • Yellow (50-74): Needs attention -- monitor closely
  • Red (0-49): At risk -- immediate intervention required

Usage:

python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json

2. churn_risk_analyzer.py

Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.

Risk Signal Weights:

Signal CategoryWeightIndicators
Usage Decline30%Login trend, feature adoption change, DAU/MAU change
Engagement Drop25%Meeting cancellations, response time, NPS change
Support Issues20%Open escalations, unresolved critical, satisfaction trend
Relationship Signals15%Champion left, sponsor change, competitor mentions
Commercial Factors10%Contract type, pricing complaints, budget cuts

Risk Tiers:

  • Critical (80-100): Immediate executive escalation
  • High (60-79): Urgent CSM intervention
  • Medium (40-59): Proactive outreach
  • Low (0-39): Standard monitoring

Usage:

python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json

3. expansion_opportunity_scorer.py

Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.

Expansion Types:

  • Upsell: Upgrade to higher tier or more of existing product
  • Cross-sell: Add new product modules
  • Expansion: Additional seats or departments

Usage:

python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json

Reference Guides

ReferenceDescription
references/health-scoring-framework.mdComplete health scoring methodology, dimension definitions, weighting rationale, threshold calibration
references/cs-playbooks.mdIntervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures
references/cs-metrics-benchmarks.mdIndustry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry

Templates

TemplatePurpose
assets/qbr_template.mdQuarterly Business Review presentation structure
assets/success_plan_template.mdCustomer success plan with goals, milestones, and metrics
assets/onboarding_checklist_template.md90-day onboarding checklist with phase gates
assets/executive_business_review_template.mdExecutive stakeholder review for strategic accounts

Best Practices

  1. Combine signals: Use all three scripts together for a complete customer picture
  2. Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
  3. Calibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md
  4. Prepare with data: Run scripts before every QBR and executive meeting; reference references/cs-playbooks.md for intervention guidance

Limitations

  • No real-time data: Scripts analyze point-in-time snapshots from JSON input files
  • No CRM integration: Data must be exported manually from your CRM/CS platform
  • Deterministic only: No predictive ML -- scoring is algorithmic based on weighted signals
  • Threshold tuning: Default thresholds are industry-standard but may need calibration for your business
  • Revenue estimates: Expansion revenue estimates are approximations based on usage patterns

Last Updated: February 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only