Portfolio — GTM Business Intelligence

Specialty Healthcare
GTM Intelligence

Seven projects spanning the full analytics maturity curve — from metric governance and standardized reporting through diagnostic analysis to AI-powered prescriptive recommendations. Built for GTM teams in the specialty EHR space.

Built by Kristen Martino · All projects use synthetic or publicly available data (CMS NPPES, Medicare PUF, Census, Salesforce-modeled) · No proprietary data

Analytics Maturity Model — Project Mapping
Governance
The foundation — metric standardization, data trust, adoption
NorthStar
Descriptive
What happened?
PracticeFlow
SpecialtyPulse
Diagnostic
Why did it happen?
ConvertPath
SpectrumIQ
SpecialtyPulse
Predictive
What will happen?
ConvertPath
Prescriptive
What should we do?
NorthStar
AskGTM
AskPractice
NewGovernance → Prescriptive
N

NorthStar

GTM analytics governance platform — metric registry with conflict resolution, reporting adoption tracker, shadow spreadsheet monitor, maturity roadmap, and a prescriptive anomaly detection engine that generates actionable alerts from metric time-series data.

GTM Decision: Whether to enforce Stage 2+ with $5K floor as the company-wide pipeline definition before next quarter's board deck — and what to do about the three teams still running their own shadow spreadsheets.
Metric StandardizationData GovernanceAdoption TrackingAnomaly DetectionPrescriptive Analytics
→
NewPrescriptive
A

AskGTM

AI-powered conversational analytics for GTM teams. Ask plain-English questions about pipeline, rep performance, win rates, and churn — get instant answers with actionable recommendations.

GTM Decision: Whether to give field sales leaders a conversational analytics layer over the GTM warehouse — trading the static dashboard tier for unprompted self-serve, and trusting the metric registry to keep the answers governed.
AI/LLM IntegrationNL QueryingPrescriptive AnalyticsGTM Intelligence
→
Diagnostic → Predictive
C

ConvertPath

Sales funnel analyzer with stage-by-stage conversion rates, source attribution, velocity analysis by deal size, weighted pipeline forecast, and leaky bucket diagnostics.

GTM Decision: Where to spend the next quarter's RevOps cycles — Lead→Qualified is the largest leak at $1.96M of recoverable pipeline, and the choice is whether SDR headcount or legal-review SLA gets the resource.
Funnel AnalysisPipeline ForecastingWin/Loss AnalysisSales Velocity
→
Diagnostic
S

SpectrumIQ

Market opportunity scoring that ranks metro areas by specialty EHR adoption potential using CMS provider density, Census demographics, and Medicare utilization.

GTM Decision: Which 5 metros to staff a specialty-EHR sales pod against next year, weighted by underserved provider density (CMS NPPES), demographic growth (Census), and Medicare utilization — and which markets to deprioritize despite high population.
Market SizingSegmentationOpportunity ScoringTAM/SAM Analysis
→
Descriptive
P

PracticeFlow

Revenue cycle benchmarking dashboard comparing practice KPIs against synthetic peer cohorts — the kind of analytics that drives EHR platform stickiness and reduces churn.

GTM Decision: Whether to make peer-cohort RCM benchmarking the centerpiece of the next renewal pitch — and which 5 KPIs (Days in A/R, denial rate, collection rate, no-show, payer mix) to anchor it on for the practice managers who'll actually open the report.
KPI DesignBenchmarkingCohort AnalysisRevenue Cycle Analytics
→
Descriptive → Diagnostic
S

SpecialtyPulse

Procedure volume and reimbursement trend monitor with auto-flagging of accelerating CPT codes — early market signals for GTM planning.

GTM Decision: Which specialty vertical to fund product investment in for the next 18 months — derm laser/Mohs has a 9% CAGR tailwind, ortho ASC migration is the wedge in joint arthroplasty, and ophtho MIGS is the highest-growth signal in the dataset.
Trend AnalysisMarket IntelligenceSignal DetectionStrategic Planning
→
Prescriptive
A

AskPractice

AI-powered practice performance tool — a practice manager asks plain-English questions about their revenue, denials, and provider productivity and gets instant answers.

GTM Decision: Whether natural-language practice analytics belongs on the SMB renewal-pitch checklist — and which questions practice managers actually ask vs. which the assistant must refuse on HIPAA grounds.
AI/LLM IntegrationNL QueryingProduct InnovationData Storytelling
→
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SQL Methodology
Production-ready SQL for every GTM metric — with edge case handling and cross-team conflict resolution notes.
📊
Q1 Performance Review
Analytical memo with segment breakdowns, root cause analysis, and prioritized recommendations — the actual deliverable.
🔧
Data Pipeline (GitHub)
Python ETL pipeline processing real CMS NPPES + Census data into SpectrumIQ opportunity scores. pandas + NumPy.
About This Portfolio

These projects are organized around a core belief: the hardest part of GTM analytics isn't building dashboards — it's building the operating model underneath them. Metric standardization, data trust, reporting adoption, and organizational readiness are prerequisites for the kind of predictive and prescriptive analytics that actually change business outcomes. That's why NorthStar (governance) and AskGTM (prescriptive AI) sit at the top — they represent the foundation and the destination.

All data is synthetic or derived from publicly available sources (CMS NPPES provider registry, Medicare Physician PUF, Census Bureau, MGMA/HFMA published benchmarks, Salesforce-modeled GTM data). No proprietary or patient data was used.

Tech stack: React, Next.js, Python data pipelines (pandas, NumPy, L2 logistic regression), Claude API (Anthropic) for natural language analytics. Each project is designed to be extensible with real data sources (Salesforce, HubSpot, billing systems, data warehouses).