Whether you want to leverage workforce data analytics to benchmark to competitors, track offshoring and outsourcing, see real-time spikes in hiring or attrition, or more, there are endless ways in which organizations are applying Revelio Labs’ available workforce data to make key strategic decisions for their business.
By ingesting the universe of public workforce data, we’ve created a standard structure to unify occupations and job titles, skills and activities, and companies and other organizations. Our team of data scientists use the latest methods in statistical research to remove sampling bias, and anticipate lags in reporting to create a comprehensive and current understanding workforce dynamics.
Revelio Labs readily available workforce data is updated monthly and delivered upon request.
Hundreds of millions of public documents
AI powered software (patented)
Universal HR database
There are five families of sources that we unify and standardize to construct a comprehensive sense of the workforce dynamics of companies: (i) online professional profiles: full time series of an individual, including dates of employment, companies, titles, education, skills, and more; (ii) job postings: posting dates, job descriptions, and salaries for each position; (iii) employee sentiment: both review scores and actual reviews; (iv) government data: published labor statistics, domestically and globally, in addition to immigration filings, census data, social security administration data, and voter registration data; and (v) firmographic data: subsidiary-parent relationships of companies, industry classifications, and mapping to financial identifiers.
Our data covers all public companies, and over 2,000,000 private companies.
Our data goes back to 2008. This is possible because each online professional profile or resume contains a full history, giving us rich longitudinal/panel data.
We deliver data on the 15th of every month, which represents the previous months counts. For example, by January 15th, we would deliver all inflows and outflows that occurred over the course of December.
We deliver our workforce data in three ways: (i) raw feed/API to all companies. Raw data is updated monthly and delivered in any desired format. Ideal for large research teams. (ii) Reports. These are sent monthly and delivered in any desired format. Ideal for fundamental investors. (iii) Access to Revelio Labs terminal for broad view of select companies. No set up or analytics required. Track by company, position, job title, seniority, and geography.
Our customers include investors, corporate strategists, HR teams, and governments.
When a company acquires or merges with another company, we choose to include the subsidiary as a part of the parent company retroactively, before the acquisition took place. Taking Amazon and Whole Foods for example, we include all Whole Foods employees as part of Amazon, during 2008-2016, even though Amazon only acquired Whole Foods in 2017. The reason for this decision is that we want to avoid seeing an artificial spike in inflows and outflows when an acquisition or spinoff occurs. Upon request, we can also provide data for companies without their subsidiaries.
Company's 10-ks typically report on employees of their company, but omit contingent workers, which in some cases can make up the majority of a company's workforce. Unlike in reported counts, we work to track all members of a workforce (employees and contingent workers) to provide a more comprehensive view of company composition and trends. For that reason, our headcounts are often (depending on industry) higher than a company's reported counts.
Because we collect data from online professional profiles, we face an issue of data being drawn from a non representative sample of the underlying population. To resolve this, we impose sampling weights to adjust for roles and locations that are underrepresented in the sample. For example, if 9/10 engineers in San Francisco have an online profile, when we see an engineer located in San Francisco, we count them as 1.1. Similarly, if 1/3 nurses in Germany have an online profile, we count them as 3. This allows us to approximate, as closely as possible, the true estimate of the underlying population.
There is a lag that exists from the time someone gets a new job to the time they report that change on their profile. Further, if someone gets laid off, they may not update their profile until they secure a new job. To account for this lag in reporting, we use a method called nowcasting where we look at periods in a company's history and see what was reported at that time and, now that the lag has disappeared, see what that reporting actually represented, and then apply that difference to the most recent period. For example, if two years ago a company reported 5 people leaving, but today we know that really 10 people had actually left at that time, we then assume in the most recent period where 10 people reported leaving, that 20 people have in fact left.
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