Job listings data can provide tremendous insight into the corporate strategies of individual companies. By tracking the job listings posted by companies each day, you can gain perspective into a management team’s thoughts and understand where they are trying to grow, their expansion strategy, and their investments. With Thinknum’s job listings dataset, now available in the Snowflake Data Marketplace, you can filter by location, position, industry, keywords, and more across 10,000 companies.
Job listings over time
Public market investors have long used job listings to predict revenue before earnings. With Snowflake and Thinknum, you can track a company’s job listings in aggregate over time to get insight on whether management plans to expand or contract. The example below shows that Vox Media decreased its job listings by 33% in one month.
The Washington Post¹ recently used Thinknum to show how 30 of the largest startups have decreased job listings by 19% since the COVID-19 pandemic started.
Track specific titles and categories of positions to understand where a company is investing. How much is management investing in technology? What specific job positions are open?
Corporate expansion by location
Use job listings to track where a company is planning to expand. Locations can be broken down by zip code, city, or country.
The following example shows that Box has its headquarters in Redwood City, but it has been expanding its secondary offices in Austin and San Francisco.
For any job listing dataset, you can use the recruitment analyzer to see which jobs have been filled and how long it took to fill them. This shows how efficient a company is at hiring, so HR managers can analyze their internal hiring timelines with the rest of the industry. For example, Zoom Video Communications has greatly improved its efficiency over the last year.
With Thinknum’s Gender Decoder tool, based on an algorithm of keywords, you can analyze the text in job listings to understand how masculine- or feminine-biased a company’s listings are.
Depending on the frequency of masculine- and feminine-coded words in job descriptions, the gender decoder assigns a rating between 0 and 100 to each job listing. A score towards 0 indicates that the language is heavily coded towards females, while a score of 100 means it is heavily skewed towards males.
The following example shows that Apple was trying to cater to a more feminine audience by using “female friendly” language in a variety of their job descriptions ranging from retail roles to hardware and even student internships.
The importance of third-party datasets
With all the data out there, it’s hard to know which sources to use. Job listings data apply broadly to every company and every industry. By accessing this data via the Snowflake Data Marketplace, you can query the data without transformation and be certain that it is the most up-to-date dataset. In addition, because the data is being accessed directly from your Snowflake account, you can easily join it with other internal datasets and accelerate decision making.
If you would like to learn more or request a trial of the job listings data, request access via your Data Marketplace account or submit a request online via the website here.