LinkedIn Company Dataset
LiveEvery row is one company, with its size, industry, location, technology stack and funding. Narrow 60M+ companies down to the accounts you actually want, and pay only for the rows you export.
Data can be accessed via
Field dictionary
Every field in the table
Types and descriptions for all 74 columns, grouped into 8 sections so you can find what you need. Filtering and previewing them in Data Explorer costs nothing.
| Field | Type | Description |
|---|---|---|
| Identity · 7 fields | ||
| _id | string | Kipplo stable record identifier |
| company_name | string | Registered or primary trading name |
| company_domain | string | Primary web domain. The join key for this dataset |
| company_linkedin | string | Company LinkedIn profile URL |
| company_description | text | Company self-description from its profile |
| specialities | array | Self-declared focus areas |
| company_name_language | string | Language the company name is written in |
| Classification · 8 fields | ||
| industry | enum | Normalised industry label |
| linkedin_industry | enum | Industry label exactly as LinkedIn reports it, before normalisation |
| naicscodes_v5 | array | NAICS v5 classification codes |
| naics_description | text | Plain-text labels for the NAICS codes |
| siccodes_v5 | array | SIC v5 classification codes |
| sic_description | text | Plain-text labels for the SIC codes |
| company_entity_type | array | Ownership type, such as public or privately held |
| company_legal_type_text | string | Registered legal form as filed |
| Size & revenue · 9 fields | ||
| headcount_range | enum | Headcount band, from 1-10 up to 10001+ |
| headcount_min | integer | Lower bound of the headcount band |
| headcount_max | integer | Upper bound of the headcount band |
| employee_on_linkedin | integer | Employees with a profile listing this company |
| employee_on_linkedin_growth_rate | float | Change in that employee count over the trailing period |
| revenue_range | enum | Estimated annual revenue band |
| revenue_min | integer | Lower bound of the revenue band, in USD |
| revenue_max | integer | Upper bound of the revenue band, in USD |
| year_founded | integer | Year the company was founded |
| Location & contact · 11 fields | ||
| company_hq_country | string | Country of the registered headquarters |
| company_hq_state | string | State or region of the headquarters |
| headquarters_city | string | City of the headquarters |
| company_country | string | Country of the primary listed location |
| company_state | string | State or region of the primary listed location |
| company_city | string | City of the primary listed location |
| company_address | string | Full street address as listed |
| postcode | string | Postal or ZIP code |
| country_region | enum | Macro region, such as APAC, EMEA or AMER |
| location_count | integer | Number of distinct listed office locations |
| company_phone | string | Primary listed contact number |
| Funding · 6 fields | ||
| total_funding_amount | integer | Disclosed funding to date, in USD |
| last_funding_amount | integer | Size of the most recent disclosed round, in USD |
| last_funding_date | date | Date of the most recent disclosed round |
| last_funding_type | enum | Round type, such as seed, series A or private equity |
| funding_round_num_investors | integer | Investors participating in that round |
| lead_investors | string | Lead investor on the most recent round |
| Technology & web · 7 fields | ||
| company_technologies | array | Detected technologies and vendors in use |
| technology_name | array | Technology names as reported by the source, before normalisation |
| technology_id | array | Identifiers for the detected technologies, for joining |
| monthly_google_adspend | integer | Estimated monthly Google Ads spend, in USD |
| total_monthly_traffic | integer | Estimated monthly visits to the company website |
| monthly_organic_traffic | integer | Share of that traffic arriving from organic search |
| monthly_paid_traffic | integer | Share of that traffic arriving from paid channels |
| Team composition · 14 fields | ||
| engineer_role_count | integer | Employees in engineer with a profile listing this company |
| devops_role_count | integer | Employees in devops with a profile listing this company |
| it_role_count | integer | Employees in it with a profile listing this company |
| security_role_count | integer | Employees in security with a profile listing this company |
| network_infrastructure_role_count | integer | Employees in network infrastructure with a profile listing this company |
| qa_role_count | integer | Employees in qa with a profile listing this company |
| mobile_dev_role_count | integer | Employees in mobile dev with a profile listing this company |
| ios_dev_role_count | integer | Employees in ios dev with a profile listing this company |
| android_dev_role_count | integer | Employees in android dev with a profile listing this company |
| sales_role_count | integer | Employees in sales with a profile listing this company |
| business_development_role_count | integer | Employees in business development with a profile listing this company |
| marketing_role_count | integer | Employees in marketing with a profile listing this company |
| customer_success_role_count | integer | Employees in customer success with a profile listing this company |
| operations_role_count | integer | Employees in operations with a profile listing this company |
| Hiring signals · 12 fields | ||
| account_executive_open_roles_count | integer | Open vacancies counted in account executive |
| business_development_open_roles_count | integer | Open vacancies counted in business development |
| customer_success_open_roles_count | integer | Open vacancies counted in customer success |
| demand_generation_open_roles_count | integer | Open vacancies counted in demand generation |
| devops_open_roles_count | integer | Open vacancies counted in devops |
| grc_open_roles_count | integer | Open vacancies counted in grc |
| it_open_roles_count | integer | Open vacancies counted in it |
| marketing_open_roles_count | integer | Open vacancies counted in marketing |
| network_infrastructure_open_roles_count | integer | Open vacancies counted in network infrastructure |
| operations_open_roles_count | integer | Open vacancies counted in operations |
| sales_open_roles_count | integer | Open vacancies counted in sales |
| security_open_roles_count | integer | Open vacancies counted in security |
All 74 columns in the dataset, grouped.
Download full data dictionary (CSV){
"_id": "1216327488716184",
"company_name": "Northwind Software",
"company_domain": "northwind.com",
"company_linkedin": "linkedin.com/company/northwind",
"industry": "B2B SaaS",
"naicscodes_v5": ["511210"],
"siccodes_v5": ["7372"],
"company_entity_type": ["Private Company"],
"headcount_range": "1001-5000",
"employee_on_linkedin": 1240,
"employee_on_linkedin_growth_rate": 2,
"revenue_range": "$50M-$100M",
"year_founded": 2011,
"company_hq_country": "United States",
"company_hq_state": "WA",
"headquarters_city": "Seattle",
"country_region": "AMER",
"location_count": 4,
"total_funding_amount": 184000000,
"last_funding_type": "Private Equity Round",
"last_funding_date": "2024-07-09",
"lead_investors": "Northstar Capital",
"company_technologies": ["Salesforce","AWS"],
"total_monthly_traffic": 412000,
"engineer_role_count": 12,
"sales_role_count": 12,
"sales_open_roles_count": 3,
"security_open_roles_count": 3
}
// 28 of 74 fields shown · the full record carries all 26 team and hiring columnsSample data
See the rows before you buy them
Real columns, in the exact shape they export.
| company_name | company_domain | industry | headcount_range | headquarters_city | company_hq_country |
|---|---|---|---|---|---|
| Northwind Software | northwind.com | B2B SaaS | 1001-5000 | Seattle | United States |
| Lumen Labs | lumenlabs.io | Analytics | 51-200 | Bengaluru | India |
| Acme Inc | acme.com | Manufacturing | 501-1000 | Milan | Italy |
| Brightpath | brightpath.co | Staffing | 201-500 | London | United Kingdom |
| Vertex | vertex.dev | Fintech | 11-50 | Seoul | South Korea |
| Harbour Retail | harbourretail.com | Retail | 1001-5000 | Sydney | Australia |
Sample rows are illustrative. Ask for a free trial to see real rows cut to your own filters before you commit.
Filters
Narrow it before you buy
The filters people reach for most. Stack them in Data Explorer, free, until the row count is where you want it.
Firmographics
- Company name
- Domain
- Industry
- Company type
- Founded year
Size & revenue
- Headcount range
- Employees on LinkedIn
- Revenue range
Location
- HQ country
- HQ state
- HQ city
- Region
Industry codes
- NAICS code
- SIC code
Funding
- Total funding
- Last round amount
- Last round date
- Lead investor
Technology & web
- Technology used
- Monthly traffic
- Organic traffic
- Paid traffic
Team & hiring
- Team size by function
- Open roles by function
Use cases
What teams build with company data at scale
Designed for teams that need thousands or millions of company records, not individual company lookups.
Size your addressable market
Count every company matching your ICP by industry, NAICS or SIC code, headcount band, revenue band and headquarters location, then size territories from actual records rather than estimates.
Map an industry and its players
Pull every company in a sector and compare them on headcount, revenue, funding, technologies in use, ad spend and website traffic. Benchmark your own position against thousands of records, not a handful.
Read hiring and team composition
See how many people a company has in engineering, sales or security, and how many roles it has open in each. Twelve in sales with three more advertised means that team is growing, and you can filter for it.
Build intelligence products
Every record carries a stable ID and a company domain, so the set joins onto your CRM, warehouse or product. Used to power research tools, internal dashboards and enrichment pipelines.
How it works
From first call to working data in four steps
It starts with a short call. You see the row count and the price before you commit. From there, filter and export your dataset in Data Explorer, query it through the API, or have it sent to your own cloud.
Tell us your segment
Industry, geography, seniority, company size, whoever you are trying to reach.
We check our coverage
A specialist checks what Kipplo actually holds for that segment and tells you how many rows match, so you know the data is there before you go any further.
Get your quote
Row count and price, before you commit.
Open your dataset
You get the complete dataset in Data Explorer and through the API, and filter it down yourself. Need it inside your own stack instead? On request we send it to your preferred cloud.
Access
How your team gets the data
Data Explorer and the API are the two ways in, and both read the identical underlying tables. If your team would rather not log in at all, we can send your filtered dataset to your own cloud on request.
Data Explorer
Data Explorer is where your team actually works with the data. Open a dataset in the grid, see the real fields and what is in them, filter down to the rows you want, then export as CSV, Excel, JSON, XML or SQL.
API access
The Kipplo API is the same data without the interface. Query records with the same filters you would use in the grid, and pull the results straight into your own product or workflow.
Delivery to your cloudOn request
Delivery is the exception, not the default route. If your team would rather not log in at all, we send your filtered dataset to your preferred cloud storage, once or on a schedule, in whatever format and structure you already work with.
Data pipeline
How we ensure quality
Every record goes through a rigorous 5-step process before it reaches you.
- 1
Data Sourcing
We gather B2B data from 50+ verified public and licensed sources.
- 2
Cleansing & De-duplication
Data is standardized, cleaned, and deduplicated on domain to eliminate inconsistencies.
- 3
AI Validation
Our Advanced models cross-check multiple data points to ensure accuracy.
- 4
Human Verification
Our data team reviews and enriches flagged records for additional reliability.
- 5
Continuous Monitoring
Data is continuously monitored and refreshed to stay current and accurate.
Before you buy
Firmographic data for 60M+ companies worldwide, including industry, headcount, HQ location, detected technologies and funding signals. One row per company domain.
No individual profiles. Every field describes the organisation, so there are no personal names, emails or profiles anywhere in the table. If you need named contacts at these companies, that is the LinkedIn Profile Dataset, which joins onto this one by domain.
We measure 95%+ field accuracy across 50+ verified sources. Every record is deduplicated, AI-validated and human-reviewed before it ships.
Yes. Talk to a data specialist and we will open Data Explorer for you, where filtering and previewing are completely free and you only pay when you export rows.
Pricing is row based. You are charged per row you export, not per data point, so the funding, technology and hiring columns on a row you already bought cost nothing extra.
Continuously. Companies are re-crawled and re-validated on an ongoing basis, so firmographic details stay current.
Yes, on request. Book a demo and we will set you up with a free trial so you can check the data before you commit.
Talk to our data experts
Every dataset starts with a short call. Tell us what you are building and a data specialist comes back within one business day.