Our Classification Methodology

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Updated: 2025
Reviewed By: SICCODE.com Industry Classification Review Team

SICCODE.com assigns verified SIC and NAICS codes using a governed workflow that combines official definitions, normalization, ML-assisted ranking, and expert review. Each assignment is explainable, versioned, and supported by lineage so organizations can use industry data confidently for analytics, compliance, and decision-making.

For formal policy and documentation, see Data Verification Policy and Methodology & Data Verification.

Overview of our industry classification process and governance controls.

Quick Facts

  • 96.8% verified accuracy across 20M+ U.S. establishments (sampled review and challenge testing)
  • Versioned assignments with change logging to preserve comparability
  • Audit-ready lineage (source, reviewer, timestamp, and rationale fields)
  • Expert review by specialists with deep SIC/NAICS experience
  • Independent recognition through academic and professional citations
Contents

Overview of the SICCODE.com classification methodology

SICCODE.com delivers industry-leading classification quality by combining authoritative SIC and NAICS frameworks, ML-assisted candidate ranking, and rigorous expert oversight. The outcome is a governed, repeatable process where each industry code assignment can be traced, explained, and versioned over time.

Objectives of verified industry classification

  • Precision: Assign the most appropriate primary industry code and log secondary activities when applicable.
  • Longitudinal consistency: Maintain stability across time so cohorts, trends, and regulatory analyses remain comparable.
  • Transparency: Store rationale metadata (reviewer notes, evidence indicators, version IDs, and confidence signals).
  • Governance: Follow documented protocols that support reproducibility and audit readiness.

Source acquisition & data normalization

  • Authoritative foundations: Official SIC and NAICS definitions, structure notes, and scope guidance anchor candidate selection.
  • Multi-source inputs: Business activities, service descriptions, products, locations, and entity structure inform classification.
  • Controlled normalization: Standard vocabularies, address cleansing, persistent IDs, and geocoding preserve lineage.
  • Deduplication: Deterministic and probabilistic methods reduce redundancy and protect entity integrity.

Process breakdown

Classification workflow

  1. Eligibility logic: Filter candidate industries that match business activity and context.
  2. Feature extraction: Convert descriptions and signals into structured features for candidate evaluation.
  3. ML-assisted ranking: Rank eligible codes with confidence signals to prioritize review.
  4. Expert human review: Specialists adjudicate exceptions and approve final assignments.

Assignment, logging & release

  1. Primary assignment: Store the final code plus rationale metadata with each record.
  2. Version control: Assign unique version IDs and retain deltas for comparability.
  3. Change documentation: Record rule updates, exception outcomes, and reviewer sign-off.
  4. Continuous improvement: Monitor drift and re-verify cohorts on a scheduled cadence.

Quality benchmarks & coverage

  • Accuracy benchmarks: 96.8% verified accuracy across 20M+ U.S. establishments (sampled review and challenge testing).
  • Enterprise usage: Used across finance, technology, public sector, and enterprise analytics workflows.
  • Validation protocol: Rolling reviews and controlled updates reduce drift and maintain quality over time.

Benchmark statements reflect internal audits and validation programs across multiple years; results are maintained through governed change control.

Governance, auditability & change management

  • Explainability: Rationale tags and confidence signals support compliance and review workflows.
  • Versioned deltas: Major updates include documentation of what changed and why.
  • Integrity controls: Persistent IDs and lineage metadata support audit requirements and reproducible reporting.

Update cadence & drift mitigation

  • Rolling updates: Improve data quality while preserving historical comparability.
  • Drift monitoring: Flag anomalies and clusters that require expert review.
  • Change logs: Maintain internal logs and publish key release notes for transparency.

Stewardship standards

  • Governance controls: Documented rules, approvals, and exception handling.
  • Policy alignment: Verification and re-verification expectations are formalized.
  • Audit readiness: Lineage fields support evidence-based reviews.

See Data Governance Framework & Stewardship Standards.

How verified methodology builds trust & authority

  • Alignment with standards: Built on official SIC/NAICS definitions and scope guidance.
  • Expert oversight: Classification decisions are reviewed and governed by specialists.
  • Auditability: Version control and lineage support audits, model governance, and compliance reporting.
  • Independent recognition: Academic and professional citations reinforce authority.

Industry classification review team

Methodology updates and rule governance are overseen by the SICCODE.com Industry Classification Review Team, including regulatory, economic, and data-quality specialists.

  • Ginger Logel — Regulatory & Industry Codes Analyst
  • Mark McNulty — Senior Industry Classification Specialist
  • Jack Francis — Lead Data Classification & Verification Analyst

For reviewer biographies and governance roles, see Industry Classification Review Team and About Our Data Team.

Cited by academic, government, and professional publications

SICCODE.com’s methodology and data standards have been referenced across peer-reviewed journals, university research, government publications, and professional textbooks. These references indicate that SICCODE.com is used as an authoritative source for SIC/NAICS definitions and industry classification in analytical and research settings.

For the verified list, see Citations & Academic Recognition.

Further reading & authoritative resources

Frequently asked questions

  • How accurate is SICCODE.com classification?
    Our governed workflow maintains 96.8% verified accuracy across 20M+ U.S. establishments through ML-assisted ranking and expert review.
  • Does SICCODE.com use machine learning for classification?
    Yes. ML-assisted ranking helps prioritize candidate codes, and specialists adjudicate exceptions before final assignment.
  • How often is the classification data updated?
    Updates occur on a rolling cadence with drift monitoring, scheduled releases, and documented change control to preserve comparability.
  • How does SICCODE.com ensure auditability and governance?
    Assignments include rationale metadata, version identifiers, and lineage controls that support reproducible reporting and audit readiness.
  • Where can I review your verification policy and standards?
    Start with Data Verification Policy and Data Governance Framework.

Related within Data Integrity & Trust:
Methodology & Data Verification
Data Verification Policy
Data Governance Framework & Stewardship Standards
Industry Classification Review Team
About Our Data Team
Citations & Academic Recognition