Dictionary: Multi-source Company Data

all personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes 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 metadata https //docs coresignal com/source documentation/multi source company data dictionary#hiumz firmographics https //docs coresignal com/source documentation/multi source company data dictionary#nq3i5 company updates https //docs coresignal com/source documentation/multi source company data dictionary#yueyx locations https //docs coresignal com/source documentation/multi source company data dictionary#ea3rt public contact details https //docs coresignal com/source documentation/multi source company data dictionary#rnxub follower counts & changes https //docs coresignal com/source documentation/multi source company data dictionary#tzh5v competitors https //docs coresignal com/source documentation/multi source company data dictionary#g7usv product overview https //docs coresignal com/source documentation/multi source company data dictionary#izuzs financials https //docs coresignal com/source documentation/multi source company data dictionary#lnakf funding https //docs coresignal com/source documentation/multi source company data dictionary#vbpw9 acquisitions https //docs coresignal com/source documentation/multi source company data dictionary#glnbh news features https //docs coresignal com/source documentation/multi source company data dictionary#hinur technographics https //docs coresignal com/source documentation/multi source company data dictionary#rr6qj company websites and social media https //docs coresignal com/source documentation/multi source company data dictionary#aew93 website traffic https //docs coresignal com/source documentation/multi source company data dictionary#izguu employee review score & changes https //docs coresignal com/source documentation/multi source company data dictionary#d8b b workforce trends https //docs coresignal com/source documentation/multi source company data dictionary#eybp3 salaries https //docs coresignal com/source documentation/multi source company data dictionary#xbui1 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 https //docs coresignal com/source documentation/multi source company data sample 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 a ll 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 is b2b indicates if the company operates in a business to business model 1 b2b company 0 b2c company boolean/integer industry company's industry string type company t ype 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\\\r\\\ncwsndhiqdq4rdgslebyqermufruvda8xgbyugbiufrt/2wbdaqmebauebqkfbqkudqsnfbqufbqu\\\r\\\nfbqufbqufbqufbqufbqufbqufbqufbqufbqufbqufbqufbqufbqufbqufbt/waarcaayadidasia\\\r\\\nahebaxeb/8qahwaaaqubaqebaqeaaaaaaaaaaaecawqfbgcicqol/8qatraaagedawieawufbaqa\\\r\\\naaf9aqidaaqrbrihmuege1fhbyjxfdkbkaeii0kxwrvs0fakm2jyggkkfhcygroljicokso0nty3\\\r\\\nodk6q0rfrkdisuptvfvwv1hzwmnkzwznaglqc3r1dnd4exqdhiwgh4ijipktljwwl5izmqkjpkwm\\\r\\\np6ipqrkztlw2t7i5usldxmxgx8jjytlt1nxw19jz2uhi4+tl5ufo6erx8vp09fb3+pn6/8qahwea\\\r\\\nawebaqebaqebaqaaaaaaaaecawqfbgcicqol/8qatreaagecbaqdbacfbaqaaqj3aaecaxeebsex\\\r\\\nbhjbuqdhcrmimoeifekrobhbcsmzuvavynlrchyknoel8rcygromjygpkju2nzg5okneruzhselk\\\r\\\nu1rvvldywvpjzgvmz2hpann0dxz3ehl6gooehyahiimkkpoulzaxmjmaoqokpaanqkmqsro0tba3\\\r\\\nulm6wspexcbhymnk0tpu1dbx2nna4upk5ebn6onq8vp09fb3+pn6/9oadambaairaxeapwd8qqkl\\\r\\\nhs57ikeskgssobq8rohijukkcxhqzigt3iqzdhv2steiyudabsxnetewagck7syxuzr9sb3oaquv\\\r\\\ntax4k8qa4qtp2h6lfqycug1s5jaujkhvlu8zvhn1b9kz7nsr2zvfsc9ppdd7tnksrmr7s4xtiznp\\\r\\\ngkaktfaa8paodnj1d+zrv7bi/lpdeq/lm+cbq+me5bgm1ua0nrzwagqcjgkhkn5cc4b9dwevoaai\\\r\\\nqkkkapav2eb3rbjwt8wfdwrej9l8k3hihw7bz2n3rltlbvlhqvncfgakorgdklkfljifeqefpif4\\\r\\\nq8g+hfhx8pdu8v6fqwg3c+i/dxii801zzyle0vpbcwxib0uuscscdwoc7s2+mamvkeejocuzjpwg\\\r\\\nd738cftaed7h/izotp4psti0ezl0dr/dcalrmpamlxpunw3ktsrlzrtidi8vnng2awugj5uv823/\\\r\\\naiz05dq2p3esq3vixqlm8tdluizpp/tqxduy5kmkumwgdppa8mgjkj4ydilq/wczxrvvcj+y9qst\\\r\\\nrtxddnm5kmt2wecgzzipgasatqzmpybeeurxtp7mp7ceh7q3wk0/xv8a8jfdwmq1++0zurw3gr2g\\\r\\\ntybhzkuuyru/fnfgwpaeopamlfrcbuvdnltx4isx8qanr0fii2bw5fabjzw+hmz1lqpay4bvocyv\\\r\\\nyxkn5n3qig6ktgviffhxdpfibw/db6bqqa6sbglqltguotz7sbf9ooakbwwcep8afllx9e+iv2d/\\\r\\\naar8ndfj1l4hfepuvc1pqcwmwelyx6yt2e1c5sgupdccnirbgymmfazknomdj4f9px9krqfgj8gf\\\r\\\nbpiepuven/rwvwwckpntbvdnsssbnnhr1lmxdgxalrhsod2ekqujr+jw/wcdszxq0525jj3vs+2j\\\r\\\n+56psflvfffqahrrrqb1exxn8tpzt2smtxdzhjznp6m5lmrq27ab44yxoxwvvu7czubenfd58kp2\\\r\\\nsfh/amgnd0zsfdn3z29lyxv/dqk1t5nmtew6qtljz8y7y42a7monen0uafr3hv8ab6+kvhzzxzza\\\r\\\nljm0solu3wlrzf2fna2v2wg7tt2fkubd8pkhxuk5d4o/tpej/i/4b0dwt4g0jw066lb2lpb6zb6p\\\r\\\nhhqjq28rijjkusl2xbjk9cea8gooakkkkaciiigaooooakkkkaciiigd/9k=", "is b2b" 1, "industry" "software development", "founded year" "2000", sic and naisc 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" 294, 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 c ategories 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 a rray of objects followers profile follower count integer date publish date s tring description published text note may contain control characters s tring reactions count number of reactions on the post integer comments count number of comments on the post integer reshared post author reshared post author s tring reshared post author url profile url of the reshared post author s tring reshared post author headline headline of the reshared post author s tring reshared post description reshared post text s tring reshared post followers the number of followers of the reshared post author integer reshared post date date the reshared post was published s tring see a snippet of the dataset for reference company updates "company updates collection" \[ { "followers" 1371, "date" "1mo", "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 string hq state headquarters state string hq street headquarters street address string hq zipcode headquarters zip code string company locations full list of company locations 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" \[ "(555) 123 4567" ], "company emails" \[ "info\@example company com" ], follower counts & changes follower counts data point description data type followers count professional network profile follower count on professional network integer followers count twitter p rofile 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 employees 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 b oolean free trial available marks if the company offers a free trial of their services b oolean demo available marks if the company offers a demo b oolean is downloadable marks if the company offers a downloadable file/service b oolean mobile apps exist marks if the company has mobile apps b oolean online reviews exist marks if the company has any online reviews b oolean api docs exist marks if the company has public api docs b oolean 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 o bjects (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) 1 number of 1 star reviews integer (long) 2 number of 2 star reviews integer (long) 3 number of 3 star reviews integer (long) 4 number of 4 star reviews integer (long) 5 number of 5 star reviews integer (long) see a snippet of the dataset for reference product review score distribution "product reviews score distribution" { "1" 0, "2" 0, "3" 4, "4" 28, "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" "$" }, "source 6 annual revenue range" { "annual revenue range from" 1 0e8, "annual revenue range to" 2 0e8, "annual revenue range currency" "$" } }, 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" "$" }, "source 1 annual revenue" { "annual revenue" 1 36025e8, "annual revenue currency" "$" } }, 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" "$" }, 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 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" "$", "date" "2023 12 29", "marketcap" 3 52990784e8 }, { "closing price" 3 680000066757202, "currency" "$", "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" "$", "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" "$", "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" "2007 06 19", "lead investors" \[ "r\&d ventures" ], "amount raised" 7100000, "amount raised currency" "$", "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" "$" }, { "acquiree name" "second acquiree", "announced date" "2020 01 27", "price" 350000000, "currency" "$" } ], "num acquisitions source 2" 2, "acquisition list source 2" \[ { "acquiree name" "first acquiree", "announced date" "2020 01 27", "price" 350000000, "currency" "$" }, { "acquiree name" "second acquiree", "announced date" "2019 12 10", "price" 350000000, "currency" "$" } ], 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 significant 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 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" } ], 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 website alias all possible company website variations (collected from our firmographic sources) string professional network url professional network 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", "website alias" \[ "primarywebsite org", "primarywebsite net", "primary site com" ] "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 o bjects (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) 1 number of 1 star reviews integer (long) 2 number of 2 star reviews integer (long) 3 number of 3 star reviews integer (long) 4 number of 4 star reviews integer (long) 5 number of 5 star reviews integer (long) see a snippet of the dataset for reference review rating distribution "employee reviews score distribution" { "1" 2, "2" 7, "3" 9, "4" 14, "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 aggregated review score by month counts available from 2022 06 array of o bjects (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 o bjects (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" "2023 01" }, { "business outlook score" 49 0, "date" "2022 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 o bjects (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" "2022 10" }, { "career opportunities score" 3 6, "date" "2022 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 o bjects (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 o bjects (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" "2023 02" }, { "compensation benefits score" 3 6, "date" "2022 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 o bjects (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" "2023 03" }, { "culture values score" 3 9, "date" "2022 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 o bjects (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" "2023 03" }, { "diversity inclusion score" 4 5, "date" "2022 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 o bjects (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 o bjects (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" "2023 03" }, { "senior management score" 3 9, "date" "2022 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 o bjects (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" "2023 01" }, { "work life balance score" 3 8, "date" "2022 09" } ], workforce trends key executives data point description data type key executives list of k ey 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" "russia", "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 o wners 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" "2019 11" }, { "employees count" 0, "date" "2021 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 titles active job posting titles array of strings see a snippet of the dataset for reference active jobs "active job postings count" 2, "active job postings titles" \[ "product manager", "devops engineer" ], 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" "2023 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" "2024 02 10" } ],