Dictionary: Multi-source Company Data

Find all data points with explanations available in the Multi-source Company data.

Each category includes a table listing the available data points, their explanations, and data types.

Data points per category

1. Metadata

2. Firmographics

3. Company updates

4. Locations

5. Public contact details

6. Follower counts & changes

7. Competitors

8. Product overview

9. Financials

10. Funding

11. Acquisitions

12. News features

13. Technographics

14. Company websites and social media

15. Website traffic

16. Employee review score & changes

17. Workforce trends

18. Salaries

The data points in the example snippets have been rearranged for better grouping. To see where a specific data point stands, check the full data sample here.

All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.

Metadata

Data point	Description	Data type
company_id	Company record identification key in our database	Integer
source_id	Identifier assigned by Professional Network	String
expired_domain	Indicates if the domain is expired	Boolean/integer
unique_domain	Indicates if the domain is unique	Boolean/integer
unique_website	Indicates if the website is unique	Boolean/integer
last_updated_at	Last update date of the record in the YYYY-MM-DD format	String (date)
created_at	Record creation date in the YYYY-MM-DD format	String (date)

See a snippet of the dataset for reference:

Metadata

"company_id": 8369825,
"source_id": "9082300",
"expired_domain": 0,
"unique_domain": 1,
"unique_website": 1,
"last_updated_at": "2024-04-28"

Firmographics

Data point	Description	Data type
company_name	Company name	String
company_name_alias	All name variations associated with the company	String
company_legal_name	Legal company name	String
company_logo	Base64-encoded image data of the company's logo	String
company_logo_url	Logo URL (available from Professional network only)	String
is_b2b	Indicates if the company operates in a business-to-business model: 1 – b2b company 0 – b2c company	Integer
industry	Company's industry	String
type	Company type	String
founded_year	Founding year	String (date)

See a snippet of the dataset for reference:

Firmographics

"company_name": "Example Company",
"company_legal_name": "Example Company, Inc.",
"company_name_alias": [
        "example-company.com",
        "Example Company"
        "Example Company, Inc. "
    ]
"company_logo": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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\\r\\nHHqjQ28Rijjkusl2XBJK9Cea8gooAKKKKACiiigAooooAKKKKACiiigD/9k=",
"company_logo_url": "https://www.professional-network.com/logo-url",
"is_b2b": 1,
"industry": "Software Development",
"founded_year": "2000",

SIC and NAICS codes

Data point	Description	Data type
sic_codes	Company's SIC codes	Array of strings
naics_codes	Company's NAICS codes	Array of strings

See a snippet of the dataset for reference:

SIC and NAISC codes

"sic_codes": [
        "87",
        "874"
      ],
"naics_codes": [
        "32",
        "325"
      ],

Descriptions

Data point	Description	Data type
description	Company description	String
description_enriched	Company description, enriched with LLM	String
description_metadata_raw	Company description (parsed from external sources not included in our firmographic data)	String

See a snippet of the dataset for reference:

Descriptions

"description": "Example Company (Nasdaq: EXMP) is a proven cloud CCaaS platform that helps business leaders redefine customer engagement and transform their contact center’s performance. Decision-makers use Example Company to improve customer experience, boost agent productivity, empower their managers, and enhance their system orchestration capabilities. Everything needed to deliver game-changing results can be seamlessly integrated and configured to maximize your success: Omnichannel Communications, AI, a Contact Center CRM, and Workforce Engagement Management tools. For more than 20 years, clients of all sizes and industries have trusted Example Company’s scalable and reliable cloud platform to power billions of omnichannel interactions every year.",
"description_enriched": "Example Company is a cloud-based call and contact center software provider that offers a range of products and solutions for businesses of all sizes. Their platform includes features such as voice, email, SMS, CRM, and workforce management, and they offer a variety of services to support their clients, including training, implementation, and consulting. ",
"description_metadata_raw": "Example Company CCaaS Ups Your Call / Contact Center Platform with Communication Software So You Can Be A Game-Changer: Voice, Email, SMS, CRM, WFM, for Inbound & Outbound Agents.",

Company size

Data point	Description	Data type
size_range	Company size based on employee count range (as selected by the company profile administrator)	String
employees_count	Number of employees on Professional Network who associated their experience with the company	Integer

See a snippet of the dataset for reference:

Company size

    "size_range": "501-1000 employees",
    "employees_count": 594,

Inferred employee counts

Data point	Description	Data type
employees_count_inferred	Estimated number of employees, calculated based on inferred employee data	Integer
employees_count_inferred_by_month	Estimated number of employees, calculated based on inferred employee data, for a three-year rolling window	Array of structs
employees_count_inferred	Estimated number of employees, calculated based on inferred employee data	Integer
date	Date identifier	String

See a snippet of the dataset for reference:

Company size

{
  "employees_count_inferred": 20,
  "employees_count_inferred_by_month": [
    {
      "employees_count_inferred": 20,
      "date": "202504"
    },
    {
      "employees_count_inferred": 18,
      "date": "202503"
    }
  ]
}

Categories & keywords

Data point	Description	Data type
categories_and_keywords	Categories and keywords assigned to the company profile and products across various platforms	Array of strings

See a snippet of the dataset for reference:

Categories and keywords

"categories_and_keywords": [
        "call/contact center software provider",
        "call & contact center software",
        "contact center software"
    ],

Ownership & status

Data point	Description	Data type
status	Operational and ownership status	Array of objects (struct)
value	Current operational status	String
comment	Current ownership status	String

See a snippet of the dataset for reference:

Ownership and status

"status": {
        "value": "active",
        "comment": "Acquired"
    },

Data point	Description	Data type
ownership_status	Ownership status	String
parent_company_information	Parent company details	Object (struct)
parent_company_name	Parent company name	String
parent_company_website	Parent company website	String
date	Date of the information provided in MM/YYYY format	String (date)

See a snippet of the dataset for reference:

Ownership and status

"ownership_status": "Public",
"parent_company_information": {
        "parent_company_name": "Parent Company",
        "parent_company_website": "https://www.parent-company.com/",
        "date": "10/2023"
    },

Company updates

Data point	Description	Data type
company_updates_collection	Information from posts published by the company	Array of objects
followers	Profile follower count	Integer
date	Publish date	String (date)
description	Published text Note: may contain control characters	String
reactions_count	Number of reactions on the post	Integer
comments_count	Number of comments on the post	Integer
reshared_post_author	Reshared post author	String
reshared_post_author_url	Profile URL of the reshared post author	String
reshared_post_author_headline	Headline of the reshared post author	String
reshared_post_description	Reshared post text	String
reshared_post_followers	The number of followers of the reshared post author	Integer
reshared_post_date	Date the reshared post was published	String

See a snippet of the dataset for reference:

Company updates

"company_updates_collection": [
      {
        "followers": 1371,
        "date": "2025-03-30",
        "description": "Example description",
        "reactions_count": 22,
        "comments_count": 2,
        "reshared_post_author": "John Doe",
        "reshared_post_author_url": "https://www.professional-network.com/john-doe",
        "reshared_post_author_headline": "Co-Founder at Example Company, TEDx & Keynote Speaker",
        "reshared_post_description": "Example description",
        "reshared_post_followers": 45,
        "reshared_post_date": "1mo"
      }
  ]

Locations

Data point	Description	Data type
hq_region	Region of the company's HQ location	Array of strings
hq_country	Country where the company's headquarters is located	String
hq_country_iso2	ISO 2-letter code of the headquarters country	String
hq_country_iso3	ISO 3-letter code of the headquarters country	String
hq_location	Headquarters location	String
hq_full_address	Full address of the headquarters	String
hq_city	Headquarters city. Data available only for US companies	String
hq_state	Headquarters state. Data available only for US companies	String
hq_street	Headquarters street address	String
hq_zipcode	Headquarters zip code	String
company_locations_full	List of company locations. Data includes raw values	Array of objects (structs)
location_address	Company location address	String
is_primary	Indicates if this is the primary company location	Boolean

See a snippet of the dataset for reference:

Locations

"hq_region": [
        "Americas",
        "Northern America",
        "AMER"
    ], 
"hq_country": "United States",
"hq_country_iso2": "US",
"hq_country_iso3": "USA",
"hq_location": "Austin, TX, United States",
"hq_full_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"hq_city": "Austin",
"hq_state": "Texas",
"hq_street": "123 Main Street; Suite 500",
"hq_zipcode": "78701",
"company_locations_full": [
        {
            "location_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
            "is_primary": true
        }
    ],

Public contact details

Data point	Description	Data type
company_phone_numbers	Public phone numbers	Array of strings
company_emails	Public email addresses	Array of strings

See a snippet of the dataset for reference:

Public contact details

"company_phone_numbers": [
    "[phone]"
],
"company_emails": [
    "[email protected]"
],

Follower counts & changes

Follower counts

Data point	Description	Data type
followers_count_professional_network	Profile follower count on professional network	Integer
followers_count_twitter	Profile follower count on Twitter	Integer (long)
followers_count_owler	Profile follower count on Owler	Integer (long)

See a snippet of the dataset for reference:

Follower counts

"followers_count_professionnal_network": 12838,
"followers_count_twitter": 705,
"followers_count_owler": 188,

Follower count changes

Data point	Description	Data type
professional_network_followers_count_change	Changes in the number of followers over different periods on professional network	Object (struct)
current	Current number of followers on the professional network	Integer (long)
change_monthly	Monthly change in follower count on the professional network	Integer (long)
change_monthly_percentage	Monthly percentage change in follower count on the professional network	Float (double)
change_quarterly	Quarterly change in follower count on the professional network	Integer (long)
change_quarterly_percentage	Quarterly percentage change in follower count on the professional network	Float (double)
change_yearly	Yearly change in follower count on the professional network	Integer (long)
change_yearly_percentage	Yearly percentage change in follower count on the professional network	Float (double)

See a snippet of the dataset for reference:

Professional network followers

"professional_network_followers_count_change": {
    "current": 12779,
    "change_monthly": 70,
    "change_monthly_percentage": 0.5507907781886852,
    "change_quarterly": 891,
    "change_quarterly_percentage": 7.494952893674293,
    "change_yearly": 1845,
    "change_yearly_percentage": 16.873971099323214
},

Data point	Description	Data type
professional_network_followers_count_by_month	Professional network follower count changes by month. Counts available from 2019.01	Array of objects (struct)
follower_count	Number of followers	Integer (long)
date	Record date	String (date)

See a snippet of the dataset for reference:

Professional network followers

"professional_network_followers_count_by_month": [
        {
            "follower_count": 0,
            "date": "2019-11"
        },
        {
            "follower_count": 1,
            "date": "2021-01"
        }
  ],

Competitors

Data point	Description	Data type
competitors	Competitors and their similarity scores	Array of objects (struct)
company_name	Competitor's name	String
similarity_score	Score indicating the similarity to the record company	Integer (long)
competitors_websites	Details on the competitors' websites	Array of objects (struct)
website	Competitor's website URL	String
total_website_visits_monthly	Total number of monthly competitor's website visits	Integer (long)
category	Competitor's website category	String
rank_category	Competitor's website rank within its category	Integer

See a snippet of the dataset for reference:

Competitors

"competitors": [
        {
            "company_name": "first competitor",
            "similarity_score": 5321
        },
        {
            "company_name": "second competitor",
            "similarity_score": 5605
        }
    ],
    "competitors_websites": [
        {
            "website": "example-website.com",
            "similarity_score": 100,
            "total_website_visits_monthly": 91600,
            "category": "Law and Government > Government",
            "rank_category": 13758
        },
        {
            "website": "example-website2.com",
            "similarity_score": 100,
            "total_website_visits_monthly": 403700,
            "category": "Law and Government > Government",
            "rank_category": 3510
        }
    ],

Product overview

Data point	Description	Data type
pricing_available	Marks if service pricing information is available online	Boolean
free_trial_available	Marks if the company offers a free trial of their services	Boolean
demo_available	Marks if the company offers a demo	Boolean
is_downloadable	Marks if the company offers a downloadable file/service	Boolean
mobile_apps_exist	Marks if the company has mobile apps	Boolean
online_reviews_exist	Marks if the company has any online reviews	Boolean
api_docs_exist	Marks if the company has public API docs	Boolean

See a snippet of the dataset for reference:

Product and services overview

"pricing_available": false,
"free_trial_available": false,
"demo_available": false,
"is_downloadable": false,
"mobile_apps_exist": false,
"online_reviews_exist": false,
"documentation_exist": false,

Product pricing

Data point	Description	Data type
product_pricing_summary	Summary of product pricing plans	Array of objects (structs)
type	Pricing plan type	String
price	Plan price	String
details	Pricing plan details	String

See a snippet of the dataset for reference:

Product pricing

"product_pricing_summary": [
    {
        "type": "First plan",
        "price": "38.00",
        "details": "Per Month"
    },
    {
        "type": "Second plan",
        "price": "85",
        "details": "per month (Annual Plan)"
    }
],

Product review scores

Data Point	Description	Data Type
product_reviews_count	Total number of product reviews	Integer (long)
product_reviews_aggregate_score	Average score of product reviews	Float (double)

See a snippet of the dataset for reference:

Product review fluctuations

    "product_reviews_count": 74,
    "product_reviews_aggregate_score": 4.513513513513513,

Data point	Description	Data type
product_reviews_score_by_month	Product review scores by month. Counts available from 2021.04	Array of objects (structs)
product_reviews_score	Product review score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Review score

"product_reviews_score_by_month": [
        {
            "product_reviews_score": 4.4,
            "date": "2019-11"
        },
        {
            "product_reviews_score": 4.6,
            "date": "2021-01"
        }
  ],

Product review score distribution

Data Point	Description	Data Type
product_reviews_score_distribution	Distribution of product review scores	Object (struct)
score_1	Number of 1-star reviews	Integer (long)
score_2	Number of 2-star reviews	Integer (long)
score_3	Number of 3-star reviews	Integer (long)
score_4	Number of 4-star reviews	Integer (long)
score_5	Number of 5-star reviews	Integer (long)

See a snippet of the dataset for reference:

Product review score distribution

    "product_reviews_score_distribution": {
        "score_1": 0,
        "score_2": 0,
        "score_3": 4,
        "score_4": 28,
        "score_5": 42
    },

Product review score changes

Data point	Description	Data type
product_reviews_score_change	Changes in the product review score over different periods	Object (struct)
current	Current product review score	Float (double)
change_monthly	Monthly change in product review score	Float (double)
change_quarterly	Quarterly change in product review score	Float (double)
change_yearly	Yearly change in product review score	Float (double)

See a snippet of the dataset for reference:

Product review fluctuations

"product_reviews_score_change": {
    "current": 4.3,
    "change_monthly": 0.0,
    "change_quarterly": 0.0,
    "change_yearly": 0.0
},

Financials

Annual revenue range

Data point	Description	Data type
revenue_annual_range	Annual revenue range information from various sources	Object (struct)
source_4_annual_revenue_range source_6_annual_revenue_range	Revenue information from a specific source	Object (struct)
annual_revenue_range_from	Minimum annual revenue range	Float (double)
annual_revenue_range_to	Maximum annual revenue range	Float (double)
annual_revenue_range_currency	Revenue currency	String

See a snippet of the dataset for reference:

Annual revenue

"revenue_annual_range": {
        "source_4_annual_revenue_range": {
            "annual_revenue_range_from": 1.0E8,
            "annual_revenue_range_to": 5.0E8,
            "annual_revenue_range_currency": "USD"
        },
"source_6_annual_revenue_range": {
            "annual_revenue_range_from": 1.0E8,
            "annual_revenue_range_to": 2.0E8,
            "annual_revenue_range_currency": "USD"
        }
    },

Annual revenue

Data point	Description	Data type
revenue_annual	Annual revenue information from various sources	Object (struct)
source_5_annual_revenue source_1_annual_revenue	Revenue information from a specific source	Object (struct)
annual_revenue	Annual revenue amount	Integer (long)
annual_revenue_currency	Revenue currency	String

See a snippet of the dataset for reference:

Annual revenue

    "revenue_annual": {
        "source_5_annual_revenue": {
            "annual_revenue": 143285000,
            "annual_revenue_currency": "USD"
        },
        "source_1_annual_revenue": {
            "annual_revenue": 1.36025E8,
            "annual_revenue_currency": "USD"
        }
    },

Quarterly revenue

Data point	Description	Data type
revenue_quarterly	Quarterly revenue information	Object (struct)
value	Quarterly revenue amount	Float (double)
currency	Revenue currency	String

See a snippet of the dataset for reference:

Quarterly revenue

    "revenue_quarterly": {
        "value": 3.5352E7,
        "currency": "USD"
    },

IPO

Data point	Description	Data type
is_public	Indicates if the company is publicly traded	Boolean
ipo_date	IPO date	String
ipo_share_price	Initial share price at the time of IPO. Value is present in USD only	Integer (long)
ipo_share_price_currency	Initial share price currency	String

See a snippet of the dataset for reference:

IPO

"is_public": 1,
"ipo_date": "2021-01-14",
"ipo_share_price": 10,
"ipo_share_price_currency": "USD",

Stock information

Data point	Description	Data type
stock_ticker	Company's stock ticker information	Array of objects (structs)
exchange	Stock exchange	String
ticker	Stock ticker	String
stock_information	Financial details of the company's stock	Array of objects (structs)
closing_price	Stock's closing price	Float (double)
currency	Stock currency	String
date	Date of the stock information in YYYY-MM-DD format	String (date)
marketcap	Market capitalization value	Float (double)

See a snippet of the dataset for reference:

Stocks

"stock_ticker": [
    {
      "exchange": "NASDAQ",
      "ticker": "AAPL"
    }
  ]
 "stock_information": [
        {
            "closing_price": 3.7300000190734863,
            "currency": "USD",
            "date": "2023-12-29",
            "marketcap": 3.52990784E8
        },
        {
            "closing_price": 3.680000066757202,
            "currency": "USD",
            "date": "2023-11-30",
            "marketcap": 3.48259008E8
        }
    ],

Income statements

Data point	Description	Data type
income_statements	Company's income statement details	Array of objects
cost_of_goods_sold	Total cost of goods sold by the company	Float (double)
cost_of_goods_sold_currency	Report currency	String
ebit	Earnings before interest and taxes	Float (double)
ebitda	Earnings before interest, taxes, depreciation, and amortization	Float (double)
ebitda_margin	EBITDA divided by total revenue	Float (double)
ebit_margin	EBIT divided by total revenue	Float (double)
earnings_per_share	Earnings per share	Float (double)
gross_profit	Profit after expenses related to manufacturing and selling its products or services	Float (double)
gross_profit_margin	Gross profit divided by revenue	Float (double)
income_tax_expense	Income tax expense	Float (double)
interest_expense	Total interest expense	Float (double)
interest_income	Interest income	Float (double)
net_income	Net income	Float (double)
period_display_end_date	Period end display date (e.g., fiscal year or quarter) based on how it's displayed in the source	String
period_end_date	Period end date in YYYY-MM-DD format	String (date)
period_type	Period type	String
pre_tax_profit	Profit before tax	Float (double)
revenue	Total revenue earned by the company	Float (double)
total_operating_expense	Total expenses related to operations	Float (double)

See a snippet of the dataset for reference:

Income statements

   "income_statements": [
        {
            "cost_of_goods_sold": 187884,
            "cost_of_goods_sold_currency": "USD",
            "ebit": 673028000,
            "ebitda": 785395000,
            "ebitda_margin": 0.23780319797984079,
            "ebit_margin": 0.20378053174514263,
            "earnings_per_share": -0.12,
            "gross_profit": 145952,
            "gross_profit_margin": 0.43719670736529315,
            "income_tax_expense": 15625,
            "interest_expense": 76,
            "interest_income": 15920,
            "net_income": 55891,
            "period_display_end_date": "Q3, 2023",
            "period_end_date": "2023-09-30",
            "period_type": "q3",
            "pre_tax_profit": 71516,
            "revenue": 333836,
            "total_operating_expense": 2.7999E7
        }
    ],

Funding

Last funding round

Data point	Description	Data type
last_funding_round_name	Last funding round name	String
last_funding_round_announced_date	Date when the last funding round was announced in YYYY-MM-DD format	String (date)
last_funding_round_lead_investors	Lead investors in the last funding round	Array of Strings
last_funding_round_amount_raised	Amount raised in the last funding round	Integer (long)
last_funding_round_amount_raised_currency	Funding round currency	String
last_funding_round_num_investors	Number of investors in the last funding round	Integer (long)

See a snippet of the dataset for reference:

Last funding round

    "last_funding_round_name": "Venture Round - Example Company",
    "last_funding_round_announced_date": "2014-03-25",
    "last_funding_round_lead_investors": [
    "John Doe Ventures"
    ],
    "last_funding_round_amount_raised": 1234567890,
    "last_funding_round_amount_raised_currency": "USD",
    "last_funding_round_num_investors": 1,

Funding rounds

Data point	Description	Data type
funding_rounds	List of completed funding rounds	Array of objects (structs)
name	Funding round name	String
announced_date	Date when the funding round was announced in YYYY-MM-DD format	String (date)
lead_investors	Lead investors in the funding round	Array of Strings
amount_raised	Amount raised in the funding round	Integer (long)
amount_raised_currency	Funding round currency	String
num_investors	Number of investors in the funding round	Integer (long)

See a snippet of the dataset for reference:

Funding rounds

    "funding_rounds": [
        {
            "name": "Series E - Example Company",
            "announced_date": "2023-06-01",
            "lead_investors": [
                "R&D Ventures"
            ],
            "amount_raised": 7000000,
            "amount_raised_currency": "USD",
            "num_investors": 3
        }
    ],

Acquisitions

Acquired by

Data point	Description	Data type
acquired_by_summary	Acquiring company details	Object (struct)
acquirer_name	Acquiring company name	String
announced_date	Acquisition date	String
price	Acquisition price	Integer (long)
currency	Acquisition currency	String

See a snippet of the dataset for reference:

Acquirer

"acquired_by_summary": {
        "acquirer_name": "Parent Company",
        "announced_date": "2023-10-04",
        "price": 350000000, 
        "currency": "USD"
    },

Acquisitions

Data point	Description	Data type
num_acquisitions_source_1 num_acquisitions_source_2 num_acquisitions_source_5	Number of completed company acquisitions based on information from various sources	Integer
acquisition_list_source_1 acquisition_list_source_2 acquisition_list_source_5	Company's acquisition information from various sources	Array of objects (struct)
acquiree_name	Acquired company name	String
announced_date	Date when the acquisition was announced in YYYY-MM-DD format	String (date)
price	Acquisition price	Integer
currency	Acquisition price currency	String

See a snippet of the dataset for reference:

Acquisitions

 "num_acquisitions_source_1": 2,
    "acquisition_list_source_1": [
        {
            "acquiree_name": "First Acquiree",
            "announced_date": "2019-12-10",
            "price": 350000000,
            "currency": "USD"
        },
        {
            "acquiree_name": "Second Acquiree",
            "announced_date": "2020-01-27",
            "price": 350000000,
            "currency": "USD"
        }
    ],
    "num_acquisitions_source_2": 2,
    "acquisition_list_source_2": [
        {
            "acquiree_name": "First Acquiree",
            "announced_date": "2020-01-27",
            "price": 350000000,
            "currency": "USD"
        },
        {
            "acquiree_name": "Second Acquiree",
            "announced_date": "2019-12-10",
            "price": 350000000,
            "currency": "USD"
        }
    ],

News features

Data point	Description	Data type
num_news_articles	Number of news articles that mention the record company	Integer
news_articles	Details about the news articles featuring company updates	Array of objects (structs)
headline	News article headline	String
published_date	Date the article was published in YYYY-MM-DD format	String (date)
summary	News article summary	String
article_url	Full news article URL	String
source	Source of the news	String

See a snippet of the dataset for reference:

Media mentions

"num_news_articles": 1,
"news_articles": [
        {
            "headline": "Example Company Layoffs Hit Channel and Sales Team",
            "published_date": "2024-01-08",
            "summary": "Professional networking sites such as have seen the influx of Example Company employees posting about getting layoff notices in the past week. The cuts have come following the acquisition by contact-center-as-a-service (CCaaS) giant NICE.",
            "article_url": "https://www.channelfutures.com/unified-communications/voicestream-technologies-inc-layoffs-hit-channel-and-sales-team",
            "source": "News source example"
        }
    ],

Technographics

Data point	Description	Data type
num_technologies_used	Number of technologies used by the company	Integer
technologies_used	List of technologies used by the company	Array of strings
technology	Technology name	String
first_verified_at	Date this technology was first assigned to the company	String (date)
last_verified_at	Date this technology was last assigned to the company	String (date)

See a snippet of the dataset for reference:

Technographics

"num_technologies_used": 40,
"technologies_used": [
    {
      "technology": "React",
      "first_verified_at": "2022-03-15",
      "last_verified_at": "2024-10-15"
    }
  ]

Company websites and social media

Data point	Description	Data type
website	Website URL	String
clean_website	Normalized domain output (e.g., http://microsoft.com)	String
website_alias	All possible company website variations (collected from our firmographic sources)	String
professional_network_url	Professional network profile URL	String
canonical_professional_network_url	Canonical company profile URL	String
twitter_url	Twitter profile URL	Array of strings
discord_url	Discord server URL	Array of strings
facebook_url	Facebook page URL	Array of strings
instagram_url	Instagram profile URL	Array of strings
pinterest_url	Pinterest profile URL	Array of strings
tiktok_url	TikTok profile URL	Array of strings
youtube_url	YouTube channel URL	Array of strings
github_url	GitHub profile URL	Array of strings
reddit_url	Reddit profile URL	Array of strings
financial_website_url	Financial network profile URL	String

See a snippet of the dataset for reference:

Company websites and social media

"website": "primarywebsite.com",
"clean_website": "http://primarywebsite.com",
"website_alias": [
    "primarywebsite.org",
    "primarywebsite.net",
    "primary-site.com"
],
"professional_network_url": "https://www.professional-network.com/company/example-company",
"canonical_professional_network_url": "https://www.professional-network.com/company/example-company",
"twitter_url": [
        "https://twitter.com/example-company"
    ],
"discord_url": [
        "https://discord.gg/example-company"
    ],
"facebook_url": [
        "https://www.facebook.com/example-company"
    ],
"instagram_url": [
        "https://www.instagram.com/example-company"
    ],
"pinterest_url": [
        "https://www.pinterest.com/example-company"
    ],
"tiktok_url": [
        "https://www.tiktok.com/@example-company"
    ],
"youtube_url": [
        "https://www.youtube.com/c/example-company"
    ],
"github_url": [
        "https://github.com/example-company"
    ],
"reddit_url": [
        "https://www.reddit.com/user/example-company"
    ],
"financial_website_url": "https://www.financial-website.com/organization/example-company",

Website traffic

Web traffic and topics

Data point	Description	Data type
total_website_visits_monthly	Monthly website visits	Integer (long)
visits_change_monthly	Monthly change in website visits, shown in percentage	Float (double)
rank_global	Global rank of the website	Integer
rank_country	Country-specific rank of the website	Integer
rank_category	Category-specific rank of the website	Integer
bounce_rate	Percentage of visitors who leave the site after visiting one page	Float (double)
pages_per_visit	Average number of pages viewed per visit	Float (double)
average_visit_duration_seconds	Average duration of a visit in seconds	Float (double)
similarly_ranked_websites	List of websites with similar rankings	Array of strings
top_topics	List of top topics associated with the website	Array of strings

See a snippet of the dataset for reference:

Web traffic and topics

"total_website_visits_monthly": 72600,
"visits_change_monthly": 14.12,
"rank_global": 573826,
"rank_country": 119057,
"rank_category": 2160,
"bounce_rate": 41.26,
"pages_per_visit": 5.5,
"average_visit_duration_seconds": 287.0,
"similarly_ranked_websites": [
        "example-website.com", 
        "examplary-website.com"
    ],
"top_topics": [
    "google",
    "social network",
    "social",
    "social media",
    "google apps"
],

Data point	Description	Data type
total_website_visits_change	Changes in the total number of website visits over different periods	Object (struct)
current	Current number of total website visits	Integer (long)
change_monthly	Monthly change in total website visits	Integer (long)
change_monthly_percentage	Monthly percentage change in total website visits	Float (double)
change_quarterly	Quarterly change in total website visits	Integer (long)
change_quarterly_percentage	Quarterly percentage change in total website visits	Float (double)
change_yearly	Yearly change in total website visits	Integer (long)
change_yearly_percentage	Yearly percentage change in total website visits	Float (double)

See a snippet of the dataset for reference:

Web traffic and topics

"total_website_visits_change": {
"current": 15432,
"change_monthly": 89,
"change_monthly_percentage": 0.576321854392679,
"change_quarterly": 1043,
"change_quarterly_percentage": 6.781293846102947,
"change_yearly": 1983,
"change_yearly_percentage": 13.425986213489573
    }

Data point	Description	Data type
total_website_visits_by_month	Website visits by month. Counts available from 202404	Array of objects (structs)
total_website_visits	Website visits	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Website visits

"total_website_visits_by_month": [
        {
            "total_website_visits": 60,
            "date": "2019-11"
        },
        {
            "total_website_visits": 75,
            "date": "2021-01"
        }
  ],

Visits by country

Data point	Description	Data type
visits_breakdown_by_country	Breakdown of website visits by country	Array of objects (structs)
country	Visitor's country	String
percentage	Percentage of visits from one country	Float (double)
percentage_monthly_change	Monthly change in percentage of visits from one country	Float (double)

See a snippet of the dataset for reference:

Visits by country

"visits_breakdown_by_country": [
    {
        "country": "United States",
        "percentage": 74.9,
        "percentage_monthly_change": 31.74
    }
],

Visits by gender

Data point	Description	Data type
visits_breakdown_by_gender	Breakdown of website visits by gender	Object (struct)
male_percentage	Percentage of visits by males	Float (double)
female_percentage	Percentage of visits by females	Float (double)

See a snippet of the dataset for reference:

Visits by gender

"visits_breakdown_by_gender": {
    "male_percentage": 64.04,
    "female_percentage": 35.96
},

Visits by age

Data point	Description	Data type
visits_breakdown_by_age	Breakdown of website visits by age group	Object (struct)
age_18_24_percentage	Percentage of visits by users aged 18-24	Float
age_25_34_percentage	Percentage of visits by users aged 25-34	Float
age_35_44_percentage	Percentage of visits by users aged 35-44	Float
age_45_54_percentage	Percentage of visits by users aged 45-54	Float
age_55_64_percentage	Percentage of visits by users aged 55-64	Float
age_65_plus_percentage	Percentage of visits by users aged 65 and above	Float

See a snippet of the dataset for reference:

Visits by age

"visits_breakdown_by_age": {
    "age_18_24_percentage": 22.92,
    "age_25_34_percentage": 32.22,
    "age_35_44_percentage": 15.47,
    "age_45_54_percentage": 13.31,
    "age_55_64_percentage": 10.3,
    "age_65_plus_percentage": 5.78
},

Employee review scores & changes

Review count

Data point	Description	Data type
company_employee_reviews_count	Total number of employee reviews	Integer (long)
company_employee_reviews_aggregate_score	Average score of employee reviews	Float (double)

See a snippet of the dataset for reference:

Review count

"company_employee_reviews_count": 145,
"company_employee_reviews_aggregate_score": 4.1,

Review score breakdown

Data point	Description	Data type
employee_reviews_score_breakdown	Breakdown of employee review ratings by category	Object (struct)
business_outlook	Business outlook rating	Float (double)
career_opportunities	Career opportunities rating	Float (double)
ceo_approval	CEO approval rating	Float (double)
compensation_benefits	Compensation and benefits rating	Float (double)
culture_values	Culture and values rating	Float (double)
diversity_inclusion	Diversity and inclusion rating	Float (double)
recommend	Recommendation rating	Float (double)
senior_management	Senior management rating	Float (double)
work_life_balance	Work-life balance rating	Float (double)

See a snippet of the dataset for reference:

Review score breakdown

"employee_reviews_score_breakdown": {
    "business_outlook": 0.55,
    "career_opportunities": 3.6,
    "ceo_approval": 0.57,
    "compensation_benefits": 4.2,
    "culture_values": 4.1,
    "diversity_inclusion": 3.7,
    "recommend": 0.76,
    "senior_management": 3.4,
    "work_life_balance": 4.2
},

Review score distribution

Data point	Description	Data type
employee_reviews_score_distribution	Distribution of star ratings in the reviews	Object (struct)
score_1	Number of 1-star reviews	Integer (long)
score_2	Number of 2-star reviews	Integer (long)
score_3	Number of 3-star reviews	Integer (long)
score_4	Number of 4-star reviews	Integer (long)
score_5	Number of 5-star reviews	Integer (long)

See a snippet of the dataset for reference:

Review rating distribution

"employee_reviews_score_distribution": {
    "score_1": 2,
    "score_2": 7,
    "score_3": 9,
    "score_4": 14,
    "score_5": 28
},

Total rating change

Data point	Description	Data type
employee_reviews_score_aggregated_change	Changes in the aggregated rating score of employee reviews over different periods	Object (struct)
current	Current aggregated score of employee reviews	Float (double)
change_monthly	Monthly change in the aggregated score	Float (double)
change_quarterly	Quarterly change in the aggregated score	Float (double)
change_yearly	Yearly change in the aggregated score	Float (double)

See a snippet of the dataset for reference:

Total rating change

"employee_reviews_score_aggregated_change": {
        "current": 4.3,
        "change_monthly": 0.05,
        "change_quarterly": 0.1,
        "change_yearly": -0.2
    }

Data point	Description	Data type
employee_reviews_score_aggregated_by_month	Aggregated review score by month Counts available from 2022.06	Array of objects (structs)
aggregated_score	Aggregated score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Aggregated reviews by month

 "employee_reviews_score_aggregated_by_month": [
        {
            "aggregated_score": 3.4,
            "date": "2023-03"
        },
        {
            "aggregated_score": 3.7,
            "date": "2024-07"
        }
    ],

Rating change in the business outlook category

Data point	Description	Data type
employee_reviews_score_business_outlook_change	Changes in the business outlook rating score	Object (struct)
current	Current business outlook score	Float (double)
change_monthly	Monthly change in the business outlook score	Float (double)
change_quarterly	Quarterly change in the business outlook score	Float (double)
change_yearly	Yearly change in the business outlook score	Float (double)

See a snippet of the dataset for reference:

Rating change in the business outlook category

"employee_reviews_score_business_outlook_change": {
        "current": 0.45,
        "change_monthly": 0.02,
        "change_quarterly": -0.05,
        "change_yearly": 0.3
    }

Data point	Description	Data type
employee_reviews_score_business_outlook_by_month	Business outlook score by month. Counts available from 2022.06	Array of objects (structs)
business_outlook_score	Business outlook score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Business outlook score by month

 "employee_reviews_score_business_outlook_by_month": [
        {
            "business_outlook_score": 49.0,
            "date": "2025-01"
        },
        {
            "business_outlook_score": 49.0,
            "date": "2024-09"
        }
],

Rating change in the career opportunities category

Data point	Description	Data type
employee_reviews_score_career_opportunities_change	Changes in the career opportunities rating score from employee reviews	Object (struct)
current	Current career opportunities score	Float (double)
change_monthly	Monthly change in the career opportunities score	Float (double)
change_quarterly	Quarterly change in the career opportunities score	Float (double)
change_yearly	Yearly change in the career opportunities score	Float (double)

See a snippet of the dataset for reference:

Rating change in the career opportunities category

"employee_reviews_score_career_opportunities_change": {
        "current": 3.8,
        "change_monthly": -0.1,
        "change_quarterly": -0.15,
        "change_yearly": -0.3
    }

Data point	Description	Data type
employee_reviews_score_career_opportunities_by_month	Career opportunities score by month. Counts available from 2022.06	Array of objects (structs)
career_opportunities_score	Business outlook score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Career opportunities score by month

"employee_reviews_score_career_opportunities_by_month": [
        {
            "career_opportunities_score": 3.6,
            "date": "2024-10"
        },
        {
            "career_opportunities_score": 3.6,
            "date": "2023-06"
        }
],

Rating change in the CEO approval category

Data point	Description	Data type
employee_reviews_score_ceo_approval_change	Changes in the CEO approval rating score from employee reviews	Object (struct)
current	Current approval score of the CEO	Float (double)
change_monthly	Monthly change in the CEO approval score	Float (double)
change_quarterly	Quarterly change in the CEO approval score	Float (double)
change_yearly	Yearly change in the CEO approval score	Float (double)

See a snippet of the dataset for reference:

Rating change in the CEO approval category

"employee_reviews_score_ceo_approval_change": {
        "current": 0.58,
        "change_monthly": -0.03,
        "change_quarterly": -0.05,
        "change_yearly": -45.12
    }

Data point	Description	Data type
employee_reviews_score_ceo_approval_by_month	CEO approval score by month. Counts available from 2022.06	Array of objects (structs)
ceo_approval_score	CEO approval score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

CEO approval score by month

"employee_reviews_score_ceo_approval_by_month": [
        {
            "ceo_approval_score": 4.0,
            "date": "2023-03"
        },
        {
            "ceo_approval_score": 3.0,
            "date": "2022-08"
        }
],

Rating change in the compensation and benefits category

Data point	Description	Data type
employee_reviews_score_compensation_benefits_change	Changes in the compensation and benefits rating score from employee reviews	Object (struct)
current	Current compensation and benefits score	Float (double)
change_monthly	Monthly change in the compensation and benefits score	Float (double)
change_quarterly	Quarterly change in the compensation and benefits score	Float (double)
change_yearly	Yearly change in the compensation and benefits score	Float (double)

See a snippet of the dataset for reference:

Rating change in the compensation and benefits category

"employee_reviews_score_compensation_benefits_change": {
        "current": 4.3,
        "change_monthly": 0.05,
        "change_quarterly": 0.07,
        "change_yearly": -0.08
    }

Data point	Description	Data type
employee_reviews_score_compensation_benefits_by_month	Compensation and benefits score by month. Counts available from 2022.06	Array of objects (structs)
compensation_benefits_score	Compensation and benefits score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Comensation benefits by month

"employee_reviews_score_compensation_benefits_by_month": [
        {
            "compensation_benefits_score": 3.6,
            "date": "2025-02"
        },
        {
            "compensation_benefits_score": 3.6,
            "date": "2024-12"
        }
],

Rating change in the culture and values category

Data point	Description	Data type
employee_reviews_score_culture_values_by_month	Culture and values score by month. Counts available from 2022.06	Array of objects (structs)
culture_values_score	Culture and values score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Culture and values score by month

"employee_reviews_score_culture_values_by_month": [
        {
            "culture_values_score": 3.9,
            "date": "2025-03"
        },
        {
            "culture_values_score": 3.9,
            "date": "2024-11"
        }
],

Data point	Description	Data type
employee_reviews_score_culture_values_change	Changes in the culture and values rating score from employee reviews	Object (struct)
current	Current culture and values score	Float (double)
change_monthly	Monthly change in the culture and values score	Float (double)
change_quarterly	Quarterly change in the culture and values score	Float (double)
change_yearly	Yearly change in the culture and values score	Float (double)

See a snippet of the dataset for reference:

Rating change in the culture and values category

"employee_reviews_score_culture_values_change": {
        "current": 4.1,
        "change_monthly": 0.1,
        "change_quarterly": 0.2,
        "change_yearly": -0.3
    }

Rating change in the diversity and inclusion category

Data point	Description	Data type
employee_reviews_score_diversity_inclusion_change	Changes in the diversity and inclusion rating score from employee reviews	Object (struct)
current	Current diversity and inclusion score	Float (double)
change_monthly	Monthly change in the diversity and inclusion score	Float (double)
change_quarterly	Quarterly change in the diversity and inclusion score	Float (double)
change_yearly	Yearly change in the diversity and inclusion score	Float (double)

See a snippet of the dataset for reference:

Rating change in the diversity and inclusion category

"employee_reviews_score_diversity_inclusion_change": {
        "current": 3.7,
        "change_monthly": -0.1,
        "change_quarterly": -0.2,
        "change_yearly": -0.3
    }

Data point	Description	Data type
employee_reviews_score_diversity_inclusion_by_month	Diversity and inclusion score by month. Counts available from 2022.06	Array of objects (structs)
diversity_inclusion_score	Diversity and inclusion score	Float (double)
date	Record date	String (date)

Diversity and inclusion score by month

"employee_reviews_score_diversity_inclusion_by_month": [
        {
            "diversity_inclusion_score": 4.5,
            "date": "2025-03"
        },
        {
            "diversity_inclusion_score": 4.5,
            "date": "2024-11"
        }
],

Rating change in the recommendations category

Data point	Description	Data type
employee_reviews_score_recommend_change	Changes in the recommendation rating score from employee reviews	Object (struct)
current	Current recommendation score	Float (double)
change_monthly	Monthly change in the recommendation score	Float (double)
change_quarterly	Quarterly change in the recommendation score	Float (double)
change_yearly	Yearly change in the recommendation score	Float (double)

See a snippet of the dataset for reference:

Rating change in the recommendations category

"employee_reviews_score_recommend_change": {
        "current": 0.76,
        "change_monthly": -0.12,
        "change_quarterly": -0.12,
        "change_yearly": -0.24
    }

Data point	Description	Data type
employee_reviews_score_recommend_by_month	Likelihood to recommend score by month. Counts available from 2022.06	Array of objects (structs)
recommend_score	Likelihood to recommend score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Recommendation score by month

"employee_reviews_score_recommend_by_month": [
        {
            "recommend_score": 0.54,
            "date": "2024-07"
        },
        {
            "recommend_score": 0.54,
            "date": "2024-06"
        }
  ],

Rating change in the senior management category

Data point	Description	Data type
employee_reviews_score_senior_management_change	Changes in the senior management rating score from employee reviews	Object (struct)
current	Current senior management score	Float (double)
change_monthly	Monthly change in the senior management score	Float (double)
change_quarterly	Quarterly change in the senior management score	Float (double)
change_yearly	Yearly change in the senior management score	Float (double)

See a snippet of the dataset for reference:

Rating change in the senior management category

"employee_reviews_score_senior_management_change": {
        "current": 3.4,
        "change_monthly": -0.1,
        "change_quarterly": -0.1,
        "change_yearly": -0.3
    }

Data point	Description	Data type
employee_reviews_score_senior_management_by_month	Senior management score by month. Counts available from 2022.06	Array of objects (structs)
senior_management_score	Senior management score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Senior management score by month

"employee_reviews_score_senior_management_by_month": [
        {
            "senior_management_score": 3.9,
            "date": "2025-03"
        },
        {
            "senior_management_score": 3.9,
            "date": "2024-11"
        }
],

Rating change in the work and life balance category

Data point	Description	Data type
employee_reviews_score_work_life_balance_change	Changes in the work-life balance rating score from employee reviews	Object (struct)
current	Current work-life balance score	Float (double)
change_monthly	Monthly change in the work-life balance score	Float (double)
change_quarterly	Quarterly change in the work-life balance score	Float (double)
change_yearly	Yearly change in the work-life balance score	Float (double)

See a snippet of the dataset for reference:

Rating change in the work and life balance category

"employee_reviews_score_work_life_balance_change": {
        "current": 4.2,
        "change_monthly": 0.0,
        "change_quarterly": 0.0,
        "change_yearly": -0.1
    }

Data point	Description	Data type
employee_reviews_score_work_life_balance_by_month	Work-life balance score by month. Counts available from 2022.06	Array of objects (structs)
work_life_balance_score	Work-life balance score	Float (double)
date	Record date	String (date)

See a snippet of the dataset for reference:

Work-life balance score by month

"employee_reviews_score_work_life_balance_by_month": [
        {
            "work_life_balance_score": 3.8,
            "date": "2025-01"
        },
        {
            "work_life_balance_score": 3.8,
            "date": "2024-09"
        }
  ],

Workforce trends

Key executives

Data point	Description	Data type
key_executives	List of key executives. Key executives are considered people who match any of the following management levels based on their position titles: Director, Head, President/Vice President, C-level, Partner, Founder, Owner	Array of objects (structs)
parent_id	Executive's identifier	String
member_full_name	Executive's name	String
member_position_title	Executive's job title	String

See a snippet of the dataset for reference:

Key executives

"key_executives": [
         {
            "parent_id": 86953887,
            "member_full_name": "John Doe",
            "member_position_title": "Partner"
         }
    ],

Key employee change events

Data point	Description	Data type
key_employee_change_events	List of key employee change events and corresponding information	Array of objects (structs)
employee_change_event_name	Employee change event	String
employee_change_event_date	Employee change event date in YYYY-MM-DD format	String (date)
employee_change_event_url	Event article URL	String

See a snippet of the dataset for reference:

Key employee change events

"key_employee_change_events": [
        {
            "employee_change_event_name": "Example Company Appoints John Doe as Chief Investment Officer",
            "employee_change_event_date": "2024-01-18",
            "employee_change_event_url" : "https://www.vcaonline.com/news/2024011822/voicestream-technologies-appoints-john-doe-as-chief-investment-officer/"
        }
    ],

Key executive arrivals

Data point	Description	Data type
key_executive_arrivals	List of new executives in the company. Executives are considered employees that have a decision_maker = true flag	Array of strings
parent_id	Employee identifier	Integer (long)
member_full_name	Full name	String
member_position_title	Position title	String
arrival_date	Start date in the position	String (date)

See a snippet of the dataset for reference:

Key employee arrivals

"key_executive_arrivals": [
        {
            "parent_id": 423235614,
            "member_full_name": "John Doe",
            "member_position_title": "Partner",
            "arrival_date": "Apr 2024"
        },
        {
            "parent_id": 2241368,
            "member_full_name": "Marry Moe",
            "member_position_title": "Partner",
            "arrival_date": "May 2024"
        }
],

Key executive departures

Data point	Description	Data type
key_executive_departures	List of former executives in the company. Executives are considered employees that have a decision_maker = true flag	Array of strings
parent_id	Employee identifier	Integer (long)
member_full_name	Full name	String
member_position_title	Position title	String
departure_date	Employment end date	String (date)

See a snippet of the dataset for reference:

Key employee departures

"key_executive_departures": [
        {
            "parent_id": 692515608,
            "member_full_name": "John Doe",
            "member_position_title": "Partner",
            "departure_date": "May 2024"
        },
        {
            "parent_id": 83299323,
            "member_full_name": "Danny Doe",
            "member_position_title": "Partner",
            "departure_date": "Aug 2024"
        }
],

Top companies

Data point	Description	Data type
top_previous_companies	Top ten companies that likely were former workplaces for the current workforce. Likely to switch counts are based on member_experience data	Array of objects
company_id	Company identification key	Integer (long)
company_name	Company name	String
count	Count to identify the number of transitions	Integer (long)
top_next_companies	Top ten companies, people will likely switch after their current job. Likely to switch counts are based on member_experience data	Array of objects
company_id	Company identification key	Integer (long)
company_name	Company name	String
count	Count to identify the number of transitions	Integer (long)

See a snippet of the dataset for reference:

Top companies

"top_previous_companies": [
    {
      "company_id": 110,
      "company_name": "Example Company",
      "count": 5
    }
  ],
  "top_next_companies": [
    {
      "company_id": 110,
      "company_name": "Example Sister Company",
      "count": 3
    }
  ]

Employee count by department

Data point	Description	Data type
employees_count_breakdown_by_department	Breakdown of employee count by department	Object (struct)
employees_count_medical	Number of employees in the medical department	Integer (long)
employees_count_sales	Number of employees in the sales department	Integer (long)
employees_count_hr	Number of employees in the HR department	Integer (long)
employees_count_legal	Number of employees in the legal department	Integer (long)
employees_count_marketing	Number of employees in the marketing department	Integer (long)
employees_count_finance	Number of employees in the finance department	Integer (long)
employees_count_tech	Number of employees in the tech department	Integer (long)
employees_count_consulting	Number of employees in the consulting department	Integer (long)
employees_count_operations	Number of employees in the operations department	Integer (long)
employees_count_other_department	Number of employees in other departments	Integer (long)
employees_count_product	Number of employees in the product department	Integer (long)

See a snippet of the dataset for reference:

Employee count by department

 "employees_count_breakdown_by_department": {
        "employees_count_medical": 0,
        "employees_count_sales": 24,
        "employees_count_hr": 8,
        "employees_count_legal": 2,
        "employees_count_marketing": 5,
        "employees_count_finance": 7,
        "employees_count_tech": 63,
        "employees_count_consulting": 2,
        "employees_count_operations": 2,
        "employees_count_other_department": 99,
        "employees_count_product": 12
    },

Data point	Description	Data type
employees_count_breakdown_by_department_by_month	Employee count changes by month and department	Object
employees_count_medical	Employee count in the medical department	Integer (long)
employees_count_sales	Employee count in the sales department	Integer (long)
employees_count_hr	Employee count in the HR department	Integer (long)
employees_count_legal	Employee count in the legal department	Integer (long)
employees_count_marketing	Employee count in the marketing department	Integer (long)
employees_count_finance	Employee count in the finance department	Integer (long)
employees_count_technical	Employee count in the technical department	Integer (long)
employees_count_consulting	Employee count in the consulting department	Integer (long)
employees_count_operations	Employee count in the operations department	Integer (long)
employees_count_product	Employee count in the product department	Integer (long)
employees_count_general_management	Employee count in the general management department	Integer (long)
employees_count_administrative	Employee count in the administrative department	Integer (long)
employees_count_customer_service	Employee count in the customer service department	Integer (long)
employees_count_project_management	Employee count in the project management department	Integer (long)
employees_count_design	Employee count in the design department	Integer (long)
employees_count_research	Employee count in the research department	Integer (long)
employees_count_trades	Employee count in the trades department	Integer (long)
employees_count_real_estate	Employee count in the real estate department	Integer (long)
employees_count_education	Employee count in the education department	Integer (long)
employees_count_other_department	Employee count in the other departments	Integer (long)
date	Record date. Counts available from 2020.01	String (date)

See a snippet of the dataset for reference:

Employee count by department

 "employees_count_breakdown_by_department_by_month": [
    {
        "employees_count_breakdown_by_department": {
            "employees_count_sales": 0,
            "employees_count_hr": 0,
            "employees_count_legal": 45,
            "employees_count_marketing": 4,
            "employees_count_finance": 0,
            "employees_count_technical": 0,
            "employees_count_consulting": 0,
            "employees_count_operations": 55,
            "employees_count_product": 0,
            "employees_count_general_management": 0,
            "employees_count_administrative": 0,
            "employees_count_customer_service": 0,
            "employees_count_project_management": 34,
            "employees_count_design": 0,
            "employees_count_research": 0,
            "employees_count_trades": 56,
            "employees_count_real_estate": 0,
            "employees_count_education": 0,
            "employees_count_other_department": 0,
            "employees_count_other_department": 5
        },
        "date": "2021-01"
    }
]

Data point	Description	Data type
employees_count_by_country	Employee count by country	Array of objects
country	Country	String
employee_count	Employee count	Integer

See a snippet of the dataset for reference:

Employee count by country

"employees_count_by_country": [
        {
            "country": "Germany",
            "employee_count": 1
        },
        {
            "country": "France",
            "employee_count": 1
        }
  ],

Data point	Description	Data type
employees_count_by_country_by_month	Employee count by country and month	Array of objects (structs)
employees_count_by_country	Employee count by country	Object (struct)
country	Country	String
employee_count	Employee count	Integer (long)
date	Record date. Counts available from 2020.01	String (date)

See a snippet of the dataset for reference:

Employee count by country

"employees_count_by_country_by_month": [
    {
        "employees_count_by_country": [
            {
                "country": "United States",
                "employee_count": 43
            }
        ],
        "date": "2021-01"
    }
]

Data point	Description	Data type
employees_count_breakdown_by_region	Employee count breakdown by region	Object
employees_count_eastern_europe	Employee count in Eastern Europe	Integer
employees_count_latin_america	Employee count in Latin America	Integer
employees_count_southern_europe	Employee count in Southern Europe	Integer
employees_count_sub_saharan_africa	Employee count in Sub-Saharan Africa	Integer
employees_count_central_asia	Employee count in Central Asia	Integer
employees_count_northern_america	Employee count in Northern America	Integer
employees_count_australia_new_zealand	Employee count in Australia and New Zealand	Integer
employees_count_northern_europe	Employee count in Northern Europe	Integer
employees_count_south_eastern_asia	Employee count in Southeast Asia	Integer
employees_count_polynesia	Employee count in Polynesia	Integer
employees_count_southern_asia	Employee count in Southern Asia	Integer
employees_count_northern_africa	Employee count in Northern Africa	Integer
employees_count_melanesia	Employee count in Melanesia	Integer
employees_count_western_europe	Employee count in Western Europe	Integer
employees_count_western_asia	Employee count in Western Asia	Integer
employees_count_eastern_asia	Employee count in Eastern Asia	Integer
employees_count_micronesia	Employee count in Micronesia	Integer
employees_count_unknown	Employee count in unassigned region	Integer

See a snippet of the dataset for reference:

Employee count by department

 "employees_count_breakdown_by_region": {
  "employees_count_eastern_europe" : 4,
  "employees_count_latin_america": 5,
  "employees_count_southern_europe": 6,
  "employees_count_sub_saharan_africa": 0,
  "employees_count_central_asia": 15,
  "employees_count_northern_america": 0,
  "employees_count_australia_new_zealand": 0,
  "employees_count_northern_europe": 0,
  "employees_count_south_eastern_asia": 0,
  "employees_count_polynesia": 2,
  "employees_count_southern_asia": 0,
  "employees_count_northern_africa": 3,
  "employees_count_melanesia": 0,
  "employees_count_western_europe": 0,
  "employees_count_western_asia": 0,
  "employees_count_eastern_asia": 9,
  "employees_count_micronesia": 0,
  "employees_count_unknown": 0
}

Data point	Description	Data type
employees_count_breakdown_by_region_by_month	Employee count breakdown by region and date	Array of objects
employees_count_breakdown_by_region	Employee count breakdown by region	Object (struct)
employees_count_eastern_europe	Employee count in Eastern Europe	Integer
employees_count_latin_america	Employee count in Latin America	Integer
employees_count_southern_europe	Employee count in Southern Europe	Integer
employees_count_sub_saharan_africa	Employee count in Sub-Saharan Africa	Integer
employees_count_central_asia	Employee count in Central Asia	Integer
employees_count_northern_america	Employee count in Northern America	Integer
employees_count_australia_new_zealand	Employee count in Australia and New Zealand	Integer
employees_count_northern_europe	Employee count in Northern Europe	Integer
employees_count_south_eastern_asia	Employee count in Southeast Asia	Integer
employees_count_polynesia	Employee count in Polynesia	Integer
employees_count_southern_asia	Employee count in Southern Asia	Integer
employees_count_northern_africa	Employee count in Northern Africa	Integer
employees_count_melanesia	Employee count in Melanesia	Integer
employees_count_western_europe	Employee count in Western Europe	Integer
employees_count_western_asia	Employee count in Western Asia	Integer
employees_count_eastern_asia	Employee count in Eastern Asia	Integer
employees_count_micronesia	Employee count in Micronesia	Integer
employees_count_unknown	Employee count in unassigned region	Integer
date	Record date. Counts available from 2020.01	String (date)

See a snippet of the dataset for reference:

Employee count by department

"employees_count_breakdown_by_region_by_month": [
    {
        "employees_count_breakdown_by_region": {
            "employees_count_eastern_europe": 3,
            "employees_count_latin_america": 0,
            "employees_count_southern_europe": 0,
            "employees_count_sub_saharan_africa": 0,
            "employees_count_central_asia": 0,
            "employees_count_northern_america": 5,
            "employees_count_australia_new_zealand": 0,
            "employees_count_northern_europe": 7,
            "employees_count_south_eastern_asia": 0,
            "employees_count_polynesia": 0,
            "employees_count_southern_asia": 0,
            "employees_count_northern_africa": 0,
            "employees_count_melanesia": 5,
            "employees_count_western_europe": 0,
            "employees_count_western_asia": 6,
            "employees_count_eastern_asia": 0,
            "employees_count_micronesia": 0,
            "employees_count_unknown": 0
        },
        "date": "2021-01"
    }
 ]

Employee count by seniority

Data point	Description	Data type
employees_count_breakdown_by_seniority	Breakdown of employee count by seniority level	Object (struct)
employees_count_owner	Number of employees with the Owner job title	Integer (long)
employees_count_founder	Number of employees with the Founder job title	Integer (long)
employees_count_clevel	Number of C-level employees	Integer (long)
employees_count_partner	Number of employees with the Partner job title	Integer (long)
employees_count_vp	Number of employees with the Vice President job title	Integer (long)
employees_count_head	Number of employees with the Head job title	Integer (long)
employees_count_director	Number of employees with the Director job title	Integer (long)
employees_count_manager	Number of employees with the Manager job title	Integer (long)
employees_count_senior	Number of senior-level employees	Integer (long)
employees_count_mid	Number of mid-level employees	Integer (long)
employees_count_junior	Number of junior-level employees	Integer (long)
employees_count_intern	Number of interns	Integer (long)
employees_count_other_management	Number of employees in other management roles	Integer (long)

See a snippet of the dataset for reference:

Employee count by seniority

    "employees_count_breakdown_by_seniority": {
        "employees_count_owner": 0,
        "employees_count_founder": 0,
        "employees_count_clevel": 3,
        "employees_count_partner": 1,
        "employees_count_vp": 8,
        "employees_count_head": 1,
        "employees_count_director": 10,
        "employees_count_manager": 31,
        "employees_count_senior": 73,
        "employees_count_mid": 8,
        "employees_count_junior": 1,
        "employees_count_intern": 0,
        "employees_count_other_management": 88
    },

Data point	Description	Data type
employees_count_breakdown_by_seniority_by_month	Employee count breakdown seniority and date	Array of objects
employees_count_breakdown_by_seniority	Employee count breakdown by seniority	Object (struct)
employees_count_owner	Number of owners in the company	Integer
employees_count_founder	Number of founders in the company	Integer
employees_count_clevel	Number of C-level employees in the company	Integer
employees_count_partner	Number of partners in the company	Integer
employees_count_vp	Number of vice presidents in the company	Integer
employees_count_head	Number of head-level employees in the company	Integer
employees_count_director	Number of directors in the company	Integer
employees_count_manager	Number of managers in the company	Integer
employees_count_senior	Number of seniors in the company	Integer
employees_count_intern	Number of interns in the company	Integer
employees_count_specialist	Number of specialists in the company	Integer
employees_count_other_management	Number of other management employees in the company	Integer
date	Record date. Counts available from 2020.01	Integer

Employee count by seniority

 "employees_count_breakdown_by_seniority_by_month": [
        {
            "employees_count_breakdown_by_seniority": {
                "employees_count_owner": 5,
                "employees_count_founder": 3,
                "employees_count_clevel": 7,
                "employees_count_partner": 2,
                "employees_count_vp": 6,
                "employees_count_head": 4,
                "employees_count_director": 10,
                "employees_count_manager": 15,
                "employees_count_senior": 20,
                "employees_count_intern": 8,
                "employees_count_specialist": 12,
                "employees_count_other_management": 5
            },
            "date": "2023-09"
        }
    ]

Employee count changes

Data point	Description	Data type
employees_count_change	Changes in the number of employees over different time periods	Object (struct)
current	Current number of employees	Integer (long)
change_monthly	Monthly change in employee count	Integer (long)
change_monthly_percentage	Monthly percentage change in employee count	Float (double)
change_quarterly	Quarterly change in employee count	Integer (long)
change_quarterly_percentage	Quarterly percentage change in employee count	Float (double)
change_yearly	Yearly change in employee count	Integer (long)
change_yearly_percentage	Yearly percentage change in employee count	Float (double)

See a snippet of the dataset for reference:

Employee count change

    "employees_count_change": {
        "current": 324,
        "change_monthly": -26,
        "change_monthly_percentage": -7.428571428571429,
        "change_quarterly": -213,
        "change_quarterly_percentage": -39.66480446927375,
        "change_yearly": -244,
        "change_yearly_percentage": -42.95774647887324
    },

Data point	Description	Data type
employees_count_by_month	Employee count changes by month. Counts available from 2019.01	Array of objects (structs)
employees_count	Number of employees	Integer (long)
date	Record date	String (date)

See a snippet of the dataset for reference:

Employees count by month

"employees_count_by_month": [
        {
            "employees_count": 0,
            "date": "2023-11"
        },
        {
            "employees_count": 0,
            "date": "2024-01"
        }
  ],

Active job postings

Data point	Description	Data type
active_job_postings_count	Number of active job postings associated with the company	Integer (long)
active_job_postings	Active job postings	Array of structs
job_posting_id	Professional network Job ID	Long
job_posting_title	Job title posted by the recruiter	String

See a snippet of the dataset for reference:

Active jobs

"active_job_postings_count": 1,
"active_job_postings_titles": [
  {
    "job_posting_id": 1234567890,
    "job_posting_title": "Product Manager",
  }
]

Data point	Description	Data type
active_job_postings_count_by_month	Active job postings by month	Object (struct)
active_job_postings_count	Job posting count	Integer (long)
date	Record date. Counts available from 2021.11	String (date)

See a snippet of the dataset for reference:

Active jobs

"active_job_postings_count_by_month": {
    "active_job_postings_count": 34,
    "date": "2024-11"
 }

Active jobs count changes

Data point	Description	Data type
active_job_postings_count_change	Changes in the number of active job postings over different periods	Object (struct)
current	Current number of active job postings	Integer (long)
change_monthly	Monthly change in active job postings count	Integer (long)
change_monthly_percentage	Monthly percentage change in active job postings count	Float (double)
change_quarterly	Quarterly change in active job postings count	Integer (long)
change_quarterly_percentage	Quarterly percentage change in active job postings count	Float (double)
change_yearly	Yearly change in active job postings count	Integer (long)
change_yearly_percentage	Yearly percentage change in active job postings count	Float (double)

See a snippet of the dataset for reference:

Changes in posted jobs

"active_job_postings_count_change": {
    "current": 11540,
    "change_monthly": 54,
    "change_monthly_percentage": 0.467822984671254,
    "change_quarterly": 743,
    "change_quarterly_percentage": 6.431215746103567,
    "change_yearly": 1594,
    "change_yearly_percentage": 14.563924765213854
}

Salaries

Base salary

Data point	Description	Data type
base_salary	List of base salary details related to a specific job title	Array of objects (structs)
title	Job title	String
salary_p25	25th percentile salary	Float (double)
salary_median	Median salary	Float (double)
salary_p75	75th percentile salary	Float (double)
currency	Salary currency	String
pay_period	Pay period	String
salary_updated_at	Date when the salary information was last updated in YYYY-MMM-DD format	String (date)

See a snippet of the dataset for reference:

Base salary by job title

    "base_salary": [
        {
            "title": "Software Engineer",
            "salary_p25": 4500000.0,
            "salary_median": 5500000.0,
            "salary_p75": 7875000.0,
            "currency": "COP",
            "pay_period": "MONTHLY",
            "salary_updated_at": "2018-05-31"
        },
        {
            "title": "Devops Engineer",
            "salary_p25": 1100000.0,
            "salary_median": 1300000.0,
            "salary_p75": 1500000.0,
            "currency": "INR",
            "pay_period": "ANNUAL",
            "salary_updated_at": "2023-01-16"
        }
    ],

Additional pay

Data point	Description	Data type
additional_pay	List of additional pay details related to a specific job title	Array of objects (structs)
title	Job title	String
additional_pay_values	Additional pay values	Array of objects (structs)
additional_pay_p25	25th percentile of additional pay	Float (double)
additional_pay_median	Median of additional pay	Float (double)
additional_pay_p75	75th percentile of additional pay	Float (double)
additional_pay_type	Additional pay type	String
currency	Pay currency	String
pay_period	Pay period	String
salary_updated_at	Date when the additional pay information was last updated in YYYY-MM-DD format	String (date)

See a snippet of the dataset for reference:

Additional pay by job title

    "additional_pay": [
        {
            "title": "Implementation Manager",
            "additional_pay_values": [
                {
                    "additional_pay_p25": 6598.52,
                    "additional_pay_median": 8798.02,
                    "additional_pay_p75": 12317.23,
                    "additional_pay_type": "Cash Bonus"
                }
            ],
            "currency": "USD",
            "pay_period": "ANNUAL",
            "salary_updated_at": "2024-02-10"
        }
    ]

Total salary

Data point	Description	Data type
total_salary	List of total salary details related to a specific job title	Array of objects (structs)
title	Job title	String
salary_p25	25th percentile salary	Float (double)
salary_median	Median salary	Float (double)
salary_p75	75th percentile salary	Float (double)
currency	Salary currency	String
pay_period	Pay period	String
salary_updated_at	Date when the salary information was last updated in YYYY-MM-DD format	String (date)

See a snippet of the dataset for reference:

Total salary by job title

    "total_salary": [
        {
            "title": "Marketing",
            "salary_p25": 45.51,
            "salary_median": 60.68,
            "salary_p75": 84.07,
            "currency": "USD",
            "pay_period": "HOURLY",
            "salary_updated_at": "2025-02-10"
        }
    ],