Employee Count Fields

Our Company Schema contains many different fields to capture the number of employees at a company. These fields are calculated using different methods, which can result in different counts for the same company.

As an example:

[Company Enrichment API Response]
   id:  exampleco
   size: 11-50
   employee_count: 76
   employee_count_by_month: 79
   sum(employee_count_by_month_by_role): 65
   sum(employee_count_by_month_by_level): 74
   sum(top_us_employee_metros): 77

[on LinkedIn] 
   116 employees

Here is how we calculate each count:

size: 11-50
This information comes from a user-selected dropdown value obtained from the company’s primary social media profile, such as LinkedIn or Facebook. Sometimes this range is far from the real count of employees, but we thought that it was important to preserve the raw selected value.

employee_count: 76
This value is the number of unique profiles where experience.company.id = ‘exampleco’ in the PDL Resume Dataset. These profiles must have the company tagged as is_primary = TRUE. This count includes profiles with null start dates, but excludes profiles with an end date before the date of the latest build.

employee_count_by_month: 79
Because we generate monthly aggregate counts using the start and end dates, this count requires a profile experience with experience.company.id = ‘exampleco’, a start date before the date of the build, and no end date.

This count does not require profiles to have the is_primary = TRUE flag, which is the main reason monthly counts will sometimes be higher than the employee_count value. In the example here, there are likely three profiles where a person currently has a job at the company that is not tagged as their primary work experience (for example an intern, investor, or advisor).

sum(employee_count_by_month_by_role): 65
We use the same criteria for this count as those in employee_count_by_month and separate counts by employees' experience.title.role.

We can only tag a subset of our profiles using a rules-based approach on job title keywords, and when we do, it is for a single role. In the future, we hope to tag more profiles by using a model-based process, specifically to match additional portions of the profiles (beyond the job title string) to the O*NET title taxonomy. While this feature is in beta, it is not yet incorporated in the aggregated company data.

sum(employee_count_by_month_by_level): 74
Like with employee_count_by_role, we can't capture a functional seniority level for every employee profile due to our deterministic tagging method, which uses the presence of specific keywords in the job titles.

The reason why the count of employees with job levels can be higher than the employee_count_by_month_by_role count is because profiles can have multiple levels. For example, someone with the title of “VP of Marketing and Director of Research” would be tagged as both job_title_levels="vp" and job_title_levels="director" in our dataset. They would be counted twice in this object, once in the "vp" level count and once in the "director" count.

sum(top_us_employee_metros): 77
We generate this count using the same profiles and filters as the employee_count_by_month field, and removing all profiles that do not have a working location in the United States.

on Linkedin: 116 employees
You can expect a slight discrepancy between our counts and those on LinkedIn. Specifically, we use a more rigorous fuzzy-matching to company names than LinkedIn, and sometimes there are delays in profile updates or even minor coverage gaps, due to the variable nature of data contributions by our Data Union members.