Value creation in software businesses comes from their human capital. Given its critical importance, Guggenheim Securities software analysts Imtiaz Koujalgi and Ken Wong aimed for a deeper understanding of how well the companies they follow are poised for revenue growth by examining staffing and hiring trends. To do this, we examined employment data compiled by Revelio Labs for a group of software companies from January 2008 through June 2020.

Employment data, which tracks people in addition to job postings, contains information on the volume of corporate hiring activity, the types of workers hired, the locations where they work, and employee churn rates.

Revelio Labs’ employment dataset provided insights into the current volume of hiring activity and quality of hiring, as the data points to certain types of employee growth being more positively correlated with revenue growth.

The findings came from exploratory data analysis, examining correlations and trends over time, and from applying machine learning algorithms. Machine learning algorithms quantified the predictive power of the data as well as identified relationships within the data. This dataset tracks 16 geographic regions and 9 job categories, for which there are 4 levels of seniority, and 5 other metrics (count, inflow, outflow, prestige and salary), resulting in 2,880 combinations, or potential data points, for any given quarter. Machine learning was helpful in identifying which of those variables, and which combination of variables, were influential. For example, strong growth in senior sales people in North America accompanied strong revenue growth while growth in junior technicians and marketing staff did not, regardless of region.

Please contact me, or Revelio Labs,  if you would like to see the report or discuss the machine learning algorithms and data science methods used.