Who buys job postings data, and what each buyer needs from it

Job postings data has five kinds of buyer: talent and workforce planning teams, sales and marketing teams, investors, economists and data platforms. This post sets out who buys it and what each buyer needs from the same records: the question asked, the fields that matter, the grain and the freshness. After it you can name the properties your own use depends on, and test a sample for them before you license a dataset.

What job postings data is, and what labour market analytics buyers get from it

Job postings data is a structured record of the roles employers advertise: one row for each posting, with the employer, the title, the location, the dates and, where the employer states it, the pay. Labour market analytics buyers get from it a dated count of hiring demand that can be cut by occupation, skill, place and week.

What a posting carries follows from how employers publish it. Google's documentation for job posting structured data lists five required properties: the title, the description, the date posted, the hiring organisation and the job location, which the guide waives for a fully remote job that states where its employees may be located. Base salary and employment type are recommended properties, as is the date a posting is valid through, which the guide requires where a posting has an expiry date. So a marked-up posting names a role, an employer, a place and a date, and states pay when the employer chooses to.

A provider adds what the employer did not publish as data: a company identifier, labels for job function and seniority, pay on one scale, and the dates the posting was first observed and closed. These additions are where datasets differ.

A posting is also a different thing from a vacancy. The US Bureau of Labor Statistics counts a job opening when three conditions hold on the last business day of the month: a specific position exists with work available for it, the job could start within 30 days, and the employer is actively recruiting from outside the establishment. The UK Office for National Statistics notes in the user guide to its online job advert statistics that an advert may hold more than one position, may be a speculative search for local talent, or may advertise training and no job. Read a posting as an employer's stated intention to hire, on the day it was published.

Who buys job postings data

Five kinds of buyer license job postings data, as this post groups them. Talent and workforce planning teams use it to plan hiring and pay. Sales and marketing teams use it to find and time accounts. Investors read it as alternative data on companies. Economists measure labour demand with it. Data platforms build it into products that they sell.

Grain is the unit a buyer analyses. Freshness is how old a record may be before it stops answering the question.

The grouping is this post's own reading, and providers name similar groups on their own sites. LinkUp, which sources job openings directly from employer websites, lists industry solutions for capital markets, human capital management, corporations, academia and education, and the public sector. Lightcast lists education, enterprise, the public sector and staffing among the industries it serves. TheirStack presents its job and technology data as hiring and technology usage signals for finding customers.

This post states no size for the market and no share for any group of buyers. We found no statistics office or regulator that publishes either.

What statistics offices and central banks do with postings

Public bodies publish how they use postings, which documents the economist's use.

• Eurostat publishes an online job advertisement rate as experimental statistics: online job advertisements from the Web Intelligence Hub, divided by the number of employees, for each occupation at the three-digit level of the ISCO classification. It notes that the same advertisement may be counted more than once.
• The Office for National Statistics publishes labour demand volumes from adverts supplied by Textkernel, as official statistics in development. It keeps one advert from each set of duplicates, the one with the earliest posting date.
• The Bank of England published a staff working paper in July 2018 that used one supplier's job adverts, posted online between 2008 and 2016, to segment the UK labour market from the text of job descriptions.
• The Federal Reserve Board published a staff note on 30 September 2026 that studies AI in manufacturing through Lightcast job postings. Its author treats postings as a timely indicator of hiring demand, and says they do not measure job openings directly.

Three needs follow for the economist's row of the table: an occupation on every record, one record for each role, and a stable method.

The job postings data market: buyers from recruiting firms to labour market data companies

Two buyers in the job postings data market sell on what they learn. Recruiting and staffing firms buy postings as a sales team does, to find employers that are hiring and advise clients on demand and pay. Labour market data companies are data platforms: they collect or license postings as raw material and sell analytics and benchmarks built from them.

A recruiting firm's unit is the employer with an open role. Lightcast's page for staffing and recruiting firms names winning new business, finding which companies are hiring for a role and setting pay levels among the uses. The firm needs the employer identified as a company, the title, the location and the first-seen date. A recruiter's own advert for a client's role is one more advert for the employer's job, so data collected from job sites can return other recruiters' adverts beside the employers' own. Finding companies that are hiring now works this use through.

A platform that builds on postings asks first what the licence permits. Internal use, embedding in a product and redistribution are three separate rights, set out in what a data licence covers. Then come the properties of a feed: stable posting and company IDs, a published schema, labels under named versions, and delivery that loads incrementally.

How talent intelligence teams use job postings data differently from investors

A talent intelligence team reads job postings across the market at one moment: the skills, roles, locations and pay that employers advertise, so that it can plan its own hiring. An investor reads them along time for one company: whether hiring is speeding up or slowing, dated as observed and tied to a security.

A talent team maps a dataset's labels to the job architecture or the public taxonomy it plans in, so it asks how each label is defined and how often it is right. A public taxonomy in Europe is ESCO, the European Commission's multilingual classification of skills, competences and occupations.

An investor's first questions are about the record. Postings are alternative data, which a US Securities and Exchange Commission release describes as the name trading firms give to information that is not contained in companies' financial statements or other traditional data sources. In that release, of September 2021, the SEC announced its first enforcement action charging an alternative data provider with securities fraud, over misrepresentations about how the provider's data was derived. So a fund's due diligence questionnaire asks where each record came from. Then the fund asks whether the history is point-in-time: dated as observed and never rewritten, so that a back-test sees what could be known on the day.

Use cases of a job postings dataset: talent intelligence vs investors and alternative data

For talent intelligence, a job postings dataset serves skills demand analysis, pay benchmarking, location planning and the study of competitors' hiring. For investors it is alternative data: hiring momentum between earnings dates, the growth of a sales team, entry into a new country, early signs of restructuring, and the count of roles in a theme such as AI.

• Talent intelligence: benchmarking pay and talent demand and reading a competitor's strategy from its hiring.
• Investors: hiring data in equity research between earnings, sales team build-out and restructuring seen early in postings.
• Sales and marketing teams: timing outreach with hiring signals.
• Economists and market researchers: sector hiring trends for macro and thematic research.

What this means when you choose a dataset

The same posting is judged on different properties by each buyer, so no single ranking of datasets holds for all five. Find your row in the first table, then run the test for it on sample data.

1. Talent teams: labels and pay. Grade fifty job function and seniority labels by hand, and count the share of sampled postings that state pay in each country you plan for. Where the labels are model output, ask which label version produced them.
2. Sales and marketing teams: the match and the age. Match your target accounts on website domain and count the matches. For one week of new postings, compute the days between the first-seen date and the day the record reached you.
3. Investors: the key and the record. Join the sample to your security master on ISIN or FIGI. Set aside the postings already published when the company was first observed. Pull the same closed day twice, some weeks apart, and compare.
4. Economists: one record for each role. Count the records that share an employer, a title and a location, and set the mix by occupation beside the official vacancy statistics of the same country. A dataset collected at each employer's own careers page holds the role as the employer published it, as why we read job postings from the company's own board sets out.
5. Data platforms: the load and the licence. Run the incremental load twice: the second run should add nothing. Read the licence for the right your product needs.

Worked example: one posting read by three buyers

Acme Robotics is an invented listed company, and this example is illustrative.

Assumptions:

• On the 14th of a month Acme Robotics publishes a posting on its own careers page: Enterprise Account Executive, Munich, hybrid, base pay of 95,000 to 125,000 euros a year. The posting names the CRM the team works in.
• The posting is observed on the day it is published. It is the company's first sales posting in Germany.
• The exchange rate is assumed at 1.10 US dollars to the euro.
• Acme Robotics starts the month with 40 open postings, 4 of them in sales. During the month it opens 18 postings, 6 of them in sales, and closes 8, 1 of them in sales.

The talent intelligence team works at another robotics company that plans a sales office in Munich. It reads the pay. On one annual US-dollar scale the range runs from 95,000 × 1.10 = 104,500 to 125,000 × 1.10 = 137,500, with a midpoint of (104,500 + 137,500) / 2 = 121,000 US dollars. One posting is one observation of advertised pay, so the team pools it with other postings for the same role and country and reads the quartiles.

The sales team sells software to revenue teams and holds Acme Robotics as a target account. It reads the event: a first enterprise sales role in a new country, opened on the 14th, naming a CRM. A rep who receives the record on the 16th acts on a signal that is 16 − 14 = 2 days old. The first-seen date and the match to the account decide the use, and the pay range plays no part.

The investor holds Acme Robotics and reads the counts that the posting moved. Open postings at the end of the month are 40 + 18 − 8 = 50, a rise of (50 − 40) / 40 = 25%. Open sales postings are 4 + 6 − 1 = 9, and their share of all open postings rises from 4 / 40 = 10% to 9 / 50 = 18%. The investor needs each figure written once, on its day, and tied to the company's ISIN.

The record is one and the same. What must be right in it depends on who reads it.

How Fokals delivers job postings data

The Fokals hiring product is company-level data throughout: collected from first-party company sources and public records, processed in-house and refreshed daily.

• Job Postings holds every role a company publishes, labelled by job function, seniority and ten role flags, with location, work mode, advertised pay on one annual US-dollar scale and the tools named.
• Hiring Activity holds daily open, new and closed postings per company. Each day is written once, after it closes, and is never revised.
• Sales Team Metrics gives weekly metrics on each company's sales organisation, and Sales Pay Benchmarks gives pay quartiles for sales roles by role and country.

Each company has one stable company ID, and each record carries the time it was observed. A listed company carries its ticker, MIC, ISIN, LEI and share-class FIGI, and its SEC CIK where it has one. Delivery is by REST API or as bulk files, under a written licence for internal use, embedding in a product or redistribution. The data dictionary defines every field of these datasets.

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What this page says about the products it names was checked against their public documentation on 7 October 2026. Product and company names are trademarks of their owners. Fokals is not affiliated with them or endorsed by them.