Essential Company Intelligence Data Points: Complete Reference
Understand the 50+ data points that make up comprehensive company intelligence. Learn what each data point means, why it matters, and how to use it for sales, marketing, and research.
What Are Company Intelligence Data Points?
Company intelligence data points are specific pieces of information about a business that help you understand its size, structure, operations, and trajectory. These data points range from basic identifiers (company name, domain) to advanced insights (funding history, technology stack, growth signals).
Think of each data point as a puzzle piece. Individually, they provide limited value. But combined, they create a comprehensive picture of a company that enables better targeting, personalization, and decision-making.
Modern company intelligence platforms aggregate 50-100+ data points per company from multiple sources. This guide covers the most important ones and explains how to use them effectively.
Category 1: Basic Identifiers
These are the fundamental data points that uniquely identify a company. They're the foundation for all other intelligence.
| Data Point | Description | Use Case |
|---|---|---|
| Company Name | Legal business name | Primary identifier, CRM matching |
| Domain | Primary website domain | Unique identifier, email validation |
| Legal Entity | Official registered name | Contract verification, compliance |
| Company ID | Unique identifier (DUNS, LEI) | Deduplication, data matching |
| Aliases | Alternative names, DBAs | Search optimization, matching |
Category 2: Firmographic Data
Firmographics are the B2B equivalent of demographics. They describe the company's basic characteristics and help you segment and target effectively.
Industry Classification
What it is: The primary industry and sub-industries the company operates in. Usually classified using standards like NAICS, SIC, or custom taxonomies.
Why it matters: Industry determines pain points, buying behavior, and solution fit. A SaaS company selling to healthcare has different needs than one selling to retail.
How to use it: Filter prospects by target industries, personalize messaging with industry-specific pain points, benchmark against industry averages.
Employee Count
What it is: Total number of employees, often provided as exact count or range (1-10, 11-50, 51-200, 201-500, 501-1000, 1000+).
Why it matters: Company size indicates budget, decision-making complexity, and solution requirements. A 50-person startup has different needs than a 5,000-person enterprise.
How to use it: Define your ideal customer profile by size, adjust pricing and packaging, route leads to appropriate sales teams (SMB vs Enterprise).
Annual Revenue
What it is: Estimated or reported annual revenue, usually in ranges (less than $1M, $1M-$10M, $10M-$50M, $50M-$100M, $100M+).
Why it matters: Revenue indicates budget capacity and purchasing power. It's a strong predictor of deal size and sales cycle length.
How to use it: Qualify leads based on budget capacity, prioritize high-revenue accounts, adjust pricing proposals.
Company Type
What it is: Legal structure and ownership type (public, private, nonprofit, government, educational).
Why it matters: Company type affects buying process, budget cycles, and decision-making. Public companies have quarterly pressures, nonprofits have budget constraints, government has procurement rules.
How to use it: Adjust sales approach based on type, time outreach to budget cycles, customize messaging for sector-specific concerns.
Year Founded
What it is: The year the company was established.
Why it matters: Company age indicates maturity, stability, and likely stage of growth. A 2-year-old startup has different priorities than a 20-year-old established business.
How to use it: Identify fast-growing young companies, target established businesses for replacement sales, adjust messaging based on maturity stage.
Category 3: Location Data
Location data helps with territory assignment, localization, and understanding market presence.
Key Location Data Points
- Headquarters Address: Primary office location (street, city, state, country, postal code)
- Office Locations: All office addresses and regional presence
- Geographic Coverage: Markets and regions served
- Time Zone: Primary time zone for outreach timing
- Country Code: ISO country code for segmentation
Use cases: Route leads to regional sales teams, time outreach appropriately, comply with regional regulations (GDPR, CCPA), localize messaging and pricing.
Category 4: Financial Data
Financial data reveals a company's economic health, growth trajectory, and purchasing power.
Funding Information
- Total Funding Raised: Cumulative capital raised across all rounds
- Latest Funding Round: Most recent round (Seed, Series A/B/C, etc.)
- Funding Amount: Amount raised in latest round
- Funding Date: When the latest round closed
- Investors: VC firms and investors backing the company
- Valuation: Company valuation (if disclosed)
Why it matters: Recent funding is a strong buying signal. Companies that just raised capital are in growth mode, hiring, and investing in new tools. They have budget and urgency.
Public Company Data
- Stock Ticker: Trading symbol
- Exchange: Stock exchange (NYSE, NASDAQ, etc.)
- Market Cap: Current market capitalization
- Stock Price: Current share price
- IPO Date: When the company went public
Category 5: People Data
Understanding who works at a company helps with targeting, personalization, and relationship building.
Leadership Information
- CEO/Founder: Name, background, LinkedIn profile
- Executive Team: C-level executives and their roles
- Key Decision Makers: VPs and directors in relevant departments
- Board Members: Board composition and advisors
Employee Metrics
- Employee Count by Department: Breakdown by engineering, sales, marketing, etc.
- Employee Growth Rate: Hiring velocity (growing, stable, shrinking)
- Employee Tenure: Average time employees stay
- Employee Locations: Where employees are based (remote, distributed, centralized)
Use cases: Identify decision makers for outreach, understand organizational structure, detect growth signals through hiring patterns, personalize messaging based on team composition.
Category 6: Technology Data (Technographics)
Technographic data reveals what technologies a company uses. This is incredibly valuable for B2B tech companies.
| Category | Examples | Why It Matters |
|---|---|---|
| CRM | Salesforce, HubSpot, Pipedrive | Integration opportunities, replacement sales |
| Marketing | Marketo, Mailchimp, Google Ads | Marketing maturity, budget allocation |
| Analytics | Google Analytics, Mixpanel, Amplitude | Data-driven culture, integration needs |
| Cloud | AWS, Azure, Google Cloud | Infrastructure preferences, technical sophistication |
| Development | React, Python, Node.js | Technical stack, developer tools needs |
Use cases: Identify companies using competitor products (replacement opportunities), find companies using complementary tools (integration opportunities), target based on tech stack compatibility, personalize demos showing relevant integrations.
Category 7: Growth Signals
Growth signals indicate companies in expansion mode—the best time to sell to them.
Hiring Signals
- Active Job Postings: Number and types of open positions
- Hiring Velocity: Rate of new job postings
- Departments Hiring: Which teams are expanding
- Job Locations: Geographic expansion signals
Why it matters: A company hiring 20 sales reps is likely investing in growth and needs tools to support that growth. A company hiring engineers needs development tools. Hiring signals reveal intent and timing.
News and Events
- Recent News: Press releases, media mentions
- Product Launches: New products or features
- Partnerships: Strategic partnerships announced
- Acquisitions: Companies acquired or merged
- Awards: Industry recognition and awards
- Office Openings: New office locations
Web Traffic and Engagement
- Website Traffic: Monthly visitors and trends
- Traffic Growth: Increasing or decreasing
- Social Media Following: LinkedIn, Twitter followers
- Social Media Growth: Follower growth rate
- Content Activity: Blog posts, webinars, podcasts
Category 8: Contact Information
Contact data enables direct outreach to companies and decision makers.
Contact Data Points
- Phone Numbers: Main line, department lines
- Email Addresses: General contact emails, department emails
- Social Media: LinkedIn, Twitter, Facebook pages
- Support Channels: Help desk, chat, support email
- Sales Contact: Sales team contact information
How to Prioritize Data Points
You don't need all 50+ data points for every use case. Prioritize based on your goals:
For Sales Prospecting
Essential: Company name, domain, industry, employee count, location
Important: Revenue, funding, decision makers, technologies used
Nice to have: Growth signals, news, social media
For Lead Scoring
Essential: Employee count, industry, revenue, location
Important: Funding, technologies, hiring signals
Nice to have: News, social media, web traffic
For Market Research
Essential: Industry, employee count, revenue, location, year founded
Important: Funding, growth rate, technologies
Nice to have: Leadership, partnerships, acquisitions
For Competitive Intelligence
Essential: Funding, employee growth, product launches, partnerships
Important: Hiring signals, news, acquisitions
Nice to have: Web traffic, social media growth, awards
Data Quality Considerations
Not all data points are equally reliable. Here's what to know:
High Accuracy Data Points
- Company name and domain (95%+ accuracy)
- Location and headquarters (90%+ accuracy)
- Industry classification (85%+ accuracy)
- Public company financials (95%+ accuracy)
Moderate Accuracy Data Points
- Employee count (70-80% accuracy, often estimated)
- Revenue (60-70% accuracy for private companies)
- Technologies used (70-80% accuracy, changes frequently)
Lower Accuracy Data Points
- Contact phone numbers (50-60% accuracy, high decay rate)
- Individual email addresses (60-70% accuracy)
- Web traffic estimates (varies widely by source)
Best practice: Use multiple data sources for critical decisions. Cross-validate important data points. Understand that some data is directional rather than precise.
Conclusion
Company intelligence data points are the building blocks of effective B2B sales, marketing, and research. Understanding what each data point means and how to use it enables better targeting, personalization, and decision-making.
Start with the essential data points for your use case. Add more as you prove value and refine your processes. Focus on data quality over quantity—accurate data on 10 key points beats inaccurate data on 50 points.
The companies that win are those that know which data points matter for their business and use them strategically to identify opportunities, prioritize efforts, and personalize engagement.