Data Accuracy Benchmarks: SICCODE vs Generic Providers

Updated: 2026 · Reviewed By: SICCODE.com Industry Classification Review Team · Editorial Neutrality Standards · Governance Standards

This page documents benchmark evidence behind SICCODE.com’s verified SIC & NAICS accuracy, cohort stability, and auditability—showing how governed, human-verified classification outperforms typical unverified directory, scraped-code, and low-cost API feeds across analytics, AI modeling, market intelligence, and compliance workflows.
Quick Facts
96.8% verified accuracy Validated benchmark (2015–2025) using expert review and challenge testing.
20M+ U.S. establishments Coverage designed for enterprise analytics, compliance, and targeting.
Cohort stability Versioned rollups reduce drift and preserve longitudinal comparability.
Independent validation See Citations & Academic Recognition.
On this page
Why Accuracy Matters SICCODE vs Generic Providers Benchmark Table Benchmarks & Impact Benchmarking Methodology Common Generic Database Issues Benefits by Use Case What Sets SICCODE Apart About FAQ

Verified SIC and NAICS classifications are foundational for analytics, AI modeling, market intelligence, and regulatory compliance. This page presents evidence for accuracy, stability, and auditability—so teams can reduce drift, improve cohort integrity, and support reproducible analysis in decision-critical environments.

Why Accuracy Matters for Analytics, AI & Compliance

Inaccurate industry classification creates downstream errors in market analysis, segmentation, forecasting, AML/KYC modeling, and regulatory reporting. Organizations relying on self-reported or keyword-derived codes often experience noisy cohorts, misaligned peer groups, unstable dashboards, and increased compliance risk.

Financial impact: Misclassification reduces targeting precision and can skew market sizing and KPIs because one incorrect label propagates into cohorts, rollups, and models.

Two Practical Risk Models (Plug In Your Numbers)
Marketing waste model Estimate wasted spend caused by misclassified targeting.
Wasted Spend ≈ Total Spend × Misclassification Rate × (1 − Match Quality)
Compliance review load model Estimate extra reviews created by misclassified entities.
Extra Reviews ≈ Total Onboardings × Misclassification Rate

Verified codes reduce these risks by providing stable, evidence-backed, audit-ready industry labels. For process details, see Our Verification Methodology.

Back to top

SICCODE.com vs Generic Providers

Generic Process (Typical)
  • Scraping or directory category ingestion
  • Keyword match → coarse mapping
  • Opaque confidence (limited explanation)
  • Unversioned updates → drift over time
SICCODE.com Process (Verified)
  • Multiple sources + normalization
  • ML-assisted candidate ranking
  • Human review/verification for high-impact cases
  • Rationale metadata + versioned releases

Definition: “Generic providers” refers to typical directory-based datasets, scraped-code feeds, and low-cost API sources that often conflate categories with official SIC/NAICS standards and do not provide governed verification, rationale metadata, or versioned change logs.

Back to top

Data Quality Benchmark Table

Metric SICCODE.com Generic Provider (Typical) Key Advantage
Accuracy rate (validated) 96.8% (verified) 1 Varies; often unpublished or estimated Reduces false positives/negatives in targeting, risk tiering, and cohort analysis
Cohort stability (time-series drift) Low (versioned rollups + deltas) 2 Medium–High (untracked changes) Maintains longitudinal integrity for forecasting and ML training sets
Auditability Rationale metadata + change logs 3 Minimal/none Supports internal/external audits and reproducible analytics
Classification evidence inputs Multi-source + governed definitions 4 Often limited to directory/site content Improves correctness for complex or hybrid businesses
Update transparency Rolling updates with deltas Irregular; no delta reporting Explains changes over time and reduces analytical breakage

Metric notes: [1] Accuracy definition · [2] Drift/stability definition · [3] Auditability definition · [4] Sources definition

Back to top

Benchmarks & Impact

  • 250,000+ organizations supported
  • 300,000+ analytics and marketing implementations analyzed
  • Full U.S. coverage with extended depth and adjacency intelligence
Illustrative Impact Examples
  • Credit risk: Misclassified cohorts can distort peer-group comparisons and risk tiering.
  • AI/ML: Cohort stability helps reduce training-set drift and improves reproducibility.
  • Compliance: Auditability supports explainable decisions in regulated workflows.

Back to top

Benchmarking Methodology

Comparison Process (High-Level)
1) Establish ground truth Use governed SIC/NAICS definitions and reviewed evidence to define primary activity for sampled entities.
2) Run challenge testing Compare SICCODE.com assignments versus generic outputs (when available) using consistent evaluation rules.
3) Measure outcomes Compute accuracy, drift/stability, auditability coverage, and update transparency.
Metric Definitions (How to Interpret This Page)
  • [1] Accuracy: agreement of primary industry assignment with a reviewed “ground truth” set defined by governed SIC/NAICS rules and expert adjudication.
  • [2] Drift/Stability: how much cohort membership changes over time due to unversioned updates or inconsistent rollups; low drift supports longitudinal analysis.
  • [3] Auditability: availability of rationale metadata, versioning/timestamps, and change logs to reproduce and explain assignments.
  • [4] Sources: breadth of evidence inputs used to support correct primary activity determination under official definitions.
  1. Governed definitions: Official SIC/NAICS definitions applied as structured interpretation rules. See Verification Methodology.
  2. Evidence normalization: Inputs are normalized and resolved to reduce duplication and improve comparability.
  3. ML + human review: Models propose candidates; senior analysts adjudicate edge cases. See About Our Data Team.
  4. Versioning: Updates are managed with change tracking to reduce drift and support reproducible rollups.

Back to top

Common Generic Database Issues

  • Keyword over-reliance: marketing language mapped to industries that don’t reflect primary activity.
  • Primary-activity confusion: secondary offerings override true principal activity.
  • Duplicate entities: HQ/branch duplication distorts counts, cohorts, and risk models.
  • Unstable rollups: unversioned updates break time-series continuity.
  • Framework misalignment: mixing SIC/NAICS rules or using outdated versions can skew reporting.

Back to top

Benefits by Use Case

Compliance & Risk Teams
  • Audit-ready evidence and reproducible change control
  • Improved sector-based screening and reporting workflows
  • Reduced false positives/negatives from misclassification
Marketing & Sales Teams
  • Cleaner segments for targeting and territory planning
  • More stable cohorts for lift measurement and attribution
  • Reduced spend waste from incorrect industry inclusion
Finance, Credit & Analytics Teams
  • Cleaner peer groups and more reliable market sizing
  • Lower drift improves forecasting and comparability
  • Higher-signal features for modeling and analysis
AI/ML & Data Science Teams
  • Reduced training-set drift and better reproducibility
  • Explainability via governance and evidence metadata
  • Improved stability across refresh cycles

Back to top

What Sets SICCODE.com Apart

  • Human-verified classification: review pathways for ambiguous or high-impact cases
  • Governed verification: documented rules, evidence handling, and escalation standards
  • Rationale metadata: the “why” behind assignments for explainability and audits
  • Versioned releases: deltas and change context to reduce cohort drift
  • Enterprise-ready structure: normalized identifiers for BI/CRM/compliance systems

Back to top

About SICCODE.com

SICCODE.com is the Center for NAICS & SIC Codes. Our classification and data governance teams support enterprises, regulators, and analytics platforms with verified data, documented lineage, and structured accuracy frameworks designed for high-stakes decision-making.

Back to top

Related Resources

FAQ

  • What does “96.8% verified accuracy” mean?
    It means SICCODE.com’s primary industry assignments met the validated benchmark in multi-industry sampling and challenge testing with expert review across 2015–2025.
  • How do you validate classification accuracy?
    Validation uses governed SIC/NAICS definitions, normalized evidence, ML-assisted candidate ranking, and human adjudication of ambiguous cases.
  • How do you prevent cohort drift over time?
    We manage updates with versioning and delta-aware release practices so changes are explicit and longitudinal comparability is preserved.