chief-customer-officer-advisor

Run retention_decomposition_analyzer.py with a JSON file of quarterly cohorts containing starting ARR, renewed ARR, expansion, and contraction. It outputs GRR, NRR, and logo retention per cohort with health verdicts and flags leaky-bucket patterns where expansion masks churn.

Use customer_segmentation_designer.py with a customer list including ARR, tenure, ICP fit signals, and support cost. It assigns each account to Strategic, Enterprise, Mid-market, or SMB tiers, scores ICP fit from 0-10, and identifies kill-list and upgrade candidates.

Run cs_coverage_calculator.py with per-tier customer counts, ARR, and current CSM staffing. It applies ARR-per-CSM and accounts-per-CSM constraints per coverage model, outputs required headcount now and in 12 months, and generates a quarterly hiring plan.

None beyond the standard library. All three scripts (retention_decomposition_analyzer.py, customer_segmentation_designer.py, cs_coverage_calculator.py) are stdlib-only and run with any Python 3 installation without pip installs.

The references recommend hiring a CSM first at Series A because proactive value realization is the early leakage point, then adding Support once ticket volume grows past roughly 30 paying customers. The stage-to-role map covers seed through late-stage sequencing.

The thresholds are B2B SaaS baselines and vary materially by ACV, segment, and industry. Consumer SaaS, marketplaces, and hardware have different retention math, so the skill explicitly disclaims applying its benchmarks outside B2B SaaS contexts.