Premium B2B Marketing Database - 18+ Million Company Records

Accelerate your B2B sales and marketing success with our comprehensive business database featuring over 18 million verified company records and 70 million employee profiles. Our 20+ year data expertise delivers superior quality and coverage compared to competitors.

Core Database Statistics

Company Records: 18,243,524 (verified businesses)

Employee Records: 70,420,010 (professional profiles)

Business Email Addresses: 38,731,006 (verified and deliverable)

Phone Numbers: 9,728,410 (direct business lines)

Geographic Coverage: Complete US business landscape

Industry Classification: Full SIC code taxonomy

Advanced Targeting Categories

Geographic Targeting: Target businesses by precise location parameters including nationwide campaigns, state-level focus, Metropolitan Service Areas (MSA), zip code radius, city and county targeting, and carrier route precision for local market penetration.

Business Profile Segmentation: Segment companies by annual revenue (sales volume), employee count (startup to enterprise), year founded (established vs. emerging), business type (small business, corporation, public company), facility ownership status, stock exchange listings (NYSE, NASDAQ, ASE), and franchise operations.

Industry Classification (SIC Codes): Leverage Standard Industrial Classification codes for precision targeting across 2-digit (broad categories), 4-digit (sub-industries), 6-digit (niche markets), and 8-digit (hyper-specific) classifications covering all major industries including Manufacturing, Healthcare, Technology, Financial Services, Professional Services, and more.

Employee & Decision Maker Targeting: Identify key decision makers by job title (C-level, VP, Director, Manager), department focus (IT, Marketing, Finance, Operations), purchasing authority levels, seniority positions, and functional roles across technical, administrative, and strategic positions.

Multi-Channel Campaign Applications

Deploy across all major B2B marketing channels:

Email Marketing: Direct outreach to verified business email addresses

LinkedIn Advertising: Professional network targeting with job title precision

Social Media: Facebook, Instagram, and Twitter/X B2B campaigns

Search Advertising: Google, BING and YouTube business targeting

Direct Mail: Physical address campaigns for high-value prospects

Telemarketing: Direct phone outreach to decision makers

Account-Based Marketing: Multi-touch ABM campaign coordination

Data Quality & Sources

Our business database aggregates from multiple verified sources:

Business registration and licensing records

Professional association memberships and directories

Industry publications and trade organizations

Conference and trade show participation data

Online business profiles and corporate websites

Financial reporting and SEC filing information

Employment databases and HR records

Technical Delivery & Integration

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download portals

Integration Options: CRM systems, marketing automation platforms, ad platforms

Custom Selections: 1,000+ selectable business and employee attributes

Update Frequency: Monthly data refreshes with real-time validation

Minimum Orders: Flexible based on targeting complexity and campaign size

Account-Based Marketing (ABM) Excellence

Specifically designed for sophisticated ABM strategies:

Target Account Identification: Find companies matching ideal customer profiles

Decision Maker Mapping: Multiple contacts within target accounts

Account Prioritization: Focus on high-revenue, high-employee companies

Personalized Outreach: Industry and company-specific messaging

Multi-Touch Coordination: Synchronized campaigns across channels

Unique Value Propositions

20+ Year Data Heritage: Established industry expertise and proven track record

Superior Data Coverage: More extensive and accurate than competitors

Real-Time Validation: Continuous data refreshing and quality assurance

Advanced Segmentation: Combine multiple targeting criteria for precision

Compliance Management: Built-in suppression lists and opt-out handling

Technical Flexibility: API access and custom integration support

Ideal Customer Profiles

Technology Companies: Software, SaaS, hardware, and IT services

Professional Services: Consulting, legal, accounting, and advisory firms

Financial Services: Banks, insurance, investment, and fintech companies

Healthcare Organizations: Medical devices, pharmaceuticals, and healthcare IT

Manufacturing Companies: Industrial equipment, automotive, and consumer goods

Marketing Agencies: Digital agencies serving B2B clients

Sales Organizations: Inside sales, field sales, and business development teams

Performance Optimization Features

Lookalike ...

Exploring Large Language Models for Automated Non-Functional Requirements Generation: A Human Annotated Dataset for NFR Quality

This artifact provides a comprehensive dataset and analysis tools for evaluating the quality of Non-Functional Requirements (NFRs) generated by Large Language Models (LLMs) based solely on Functional Requirements (FRs). The dataset includes human evaluations of NFR quality according to ISO/IEC 25010:2023 standard quality attributes.

Description

This research artifact contains:

• Human evaluation data for NFRs generated by 8 different LLMs across 34 functional requirements
• Professional responses collected through Turso database service from software engineering professionals
• Analysis scripts for data processing and statistical analysis
• LLM outputs in structured JSON format for all tested models
• Advanced prompting techniques incorporating ISO/IEC 25010:2023 standards

The study evaluates two key aspects:

1. NFR Validity (1-5 scale): Coherence and appropriateness of generated NFRs
2. Attribute Applicability (1-5 scale): Relevance of assigned ISO quality attributes

Requirements

Software Dependencies

• Deno (JavaScript/TypeScript runtime) - Version 1.40+ recommended
• SQLite3 (for database operations)
• Standard text editor (for viewing TSV/JSON files)

Hardware Requirements

• RAM: Minimum 4GB (recommended 8GB)
• Storage: 500MB free space
• OS: Cross-platform (Linux, macOS, Windows)

Installation

1. Install Deno:

# Linux/macOS
curl -fsSL https://deno.land/install.sh | sh

# Windows (PowerShell)
irm https://deno.land/install.ps1 | iex

1. Verify Installation:

deno --version

1. Clone/Download Artifact: Extract downloaded archive

Step-by-Step Instructions to Reproduce Paper Results

Step 1: Examine Raw Data Sources

Input: Professional evaluation data collected via Turso database service

• File: data/dump.sql
• Description: SQL dump containing responses from software engineering professionals who evaluated LLM-generated NFRs
• Content: Raw evaluation data including validity scores (1-5), applicability scores (1-5), and quality attribute assignments

Expected Output: Understanding of data collection methodology and raw response structure

Step 2: Generate Analysis Database

Purpose: Convert SQL dump to SQLite database for analysis

cd analysis
deno run --allow-read --allow-write generateData.ts

Process:

• The generateData.ts script reads data/dump.sql
• Creates data/dump.db SQLite database
• Structures data for statistical analysis

Expected Output: data/dump.db file created (approximately 2-5MB)

Step 3: Process and Merge Evaluation Data

Purpose: Combine human evaluations with LLM assignments and generate final dataset

The generateData.ts script performs:

1. Assignment Processing: Maps evaluators to specific FR-LLM combinations:
   • NFR Validity evaluations: 10 evaluators × 3 FRs each × 8 LLMs
   • Attribute Applicability evaluations: 10 evaluators × 3 FRs each × 8 LLMs
2. Data Merging: Combines database records with assignment metadata
3. CSV Generation: Outputs structured TSV file for analysis

Expected Output: analysis/Human_Evaluation_Data.tsv (final dataset used in paper)

Step 4: Analyze LLM Output Structure

Files: LLMOutputs/*.json (8 files, one per LLM)

• claude-3-5-haiku.json
• claude-3-7-sonnet.json
• deepSeek-V3.json
• gemini-1.5-pro.json
• gpt-4o-mini.json
• grok-2.json
• lama-3.3-70B.json
• Qwen2.5-72B.json

Expected Format for each FR:

{
 "functionalRequirement": "System shall allow users to log in with username and password",
 "identifiedNFRs": [
  {
   "attribute": "Security",
   "requirement": "The system must encrypt passwords using AES-256 encryption",
   "justification": "Login functionality requires secure credential handling"
  }
 ]
}

Analysis: Each JSON contains 34 FR entries with generated NFRs following ISO/IEC 25010:2023 categories

Step 5: Examine Prompt Engineering Approach

File: data/AdvancedPrompt.txt Content: Complete prompt used for NFR generation including:

• Role assignment (expert software quality engineer)
• Knowledge grounding (ISO/IEC 25010:2023 standard)
• Output structure constraints (JSON format)
• Quality requirements (specific, actionable, testable NFRs)

File Descriptions

Core Dataset Files

• analysis/Human_Evaluation_Data.tsv: Main evaluation dataset (2,240 evaluated NFRs)
  • Columns: FR ID, FR text, NFR ID, LLM model, ISO attribute, NFR text, justification, validity score, applicability score, human attribute assignment, evaluator assignment type, evaluator ID
• data/FR_34.tsv: 34 functional requirements subset used for evaluation
• data/dump.sql: Raw SQL dump from Turso database service containing professional evaluations

LLM Output Files

• LLMOutputs/[model].json: Structured NFR generations for each of 8 LLMs
  • Each file contains 34 FR entries with associated NFRs in JSON format

Configuration Files

• data/AdvancedPrompt.txt: Complete prompt template with ISO/IEC 25010:2023 integration
• analysis/generateData.ts: Data processing script for database creation and CSV generation

Documentation

• LICENSE.md: Distribution rights and usage terms
• analysis/visualization.ipynb: Jupyter notebook for data visualization and statistical analysis

Mapping to Paper Claims

Key Paper Statistics (Section 6 - Results)

• 1,593 total NFRs generated across 8 LLMs and 34 FRs
• 174 NFRs evaluated for validity and applicability scoring
• 168 NFRs evaluated for attribute selection task
• Mean validity score: 4.63 (median: 5.0) on 1-5 scale
• Mean applicability score: 4.59 (median: 5.0) on 1-5 scale
• 80.4% attribute accuracy in expert vs. LLM attribute selection

Figure Reproduction Mapping

• Figure 3: Reproduced from validity scores in Human_Evaluation_Data.tsv
  • Shows 90.8% of NFRs scored ≥4, with 76.4% scoring perfect 5
• Figure 4: Generated from applicability scores in Human_Evaluation_Data.tsv
  • Demonstrates 90.2% highly applicable ratings (scores 4-5)
• Figure 5: Computed from attribute selection task data
  • Visualizes 80.4% exact matches, 8.3% near misses, 11.3% complete mismatches
• Figure 6: Generated from LLM vs. expert attribute assignments
  • Shows specific misclassification patterns (e.g., Functional Suitability vs. Reliability)

Table Reproduction Mapping

• Table 4 (LLM Comparison): Directly derived from Human_Evaluation_Data.tsv grouped by LLM model
  • Validity ranges: 3.96 (claude-3-7-sonnet) to 4.94 (llama-3.3-70B)
  • Applicability ranges: 3.67 (claude-3-7-sonnet) to 4.97 (grok-2)
  • Attribute accuracy ranges: 71.4% (deepSeek-V3) to 90.9% (gemini-1.5-pro)

Research Questions Validation

• RQ1 (LLM Effectiveness): Validated through high validity (90.8% ≥4) and applicability (90.2% ≥4) scores
• RQ2 (Best Performing LLM): Answered via Table 4 comparison showing gemini-1.5-pro (highest attribute accuracy) and llama-3.3-70B (highest validity/applicability)
• RQ3 (Prompting Technique Impact): Demonstrated through advanced vs. baseline prompting comparison

Methodology Reproduction (Section