- At-a-glance demo
- Role Allocation
- Change Over Time
- Hires and Exits
- Sources and Destinations
- Educational Background
Role Allocation chart allows users to see total current employee counts by company. Employees will also be assigned to role segments. A useful toggle is switching
100% Stacked in the top left drop down to compare role ratios across benchmark companies.
This chart includes all profiles that have a start date and no specified end date
Change Over Time
Change Over Time charts allow the user to analyze headcount changes by year. The stacked bar version further disaggregates data by role segment and offers monthly resolution.
These charts include all profiles that have a start date / end date combination that spans the entire time interval shown.
- Jane was an engineer for Company A between January 1st, 2019 to February 1st, 2021
- Jane would be counted as an employee for each month starting January, 2019 (inclusive) until February 2021 (exclusive)
Hires and Exits
Hires and Exits further disaggregates time series analysis by decoupling inflows from outflows. This analysis can help to identify whether net growth (or loss) is driven by arrivals or departures.
By default, these charts are configured on a monthly basis. Users may want to aggregate in quarterly or annual increments in post analysis to smoothen season effects.
Some common season effects:
- A minority of public profiles only use year (no dates) and will therefore by default be counted in January of each year
- Some companies have systemically high inflows at the beginning of summer months and systemically high outflows at the end of summer months for internship programs
Sources and Destinations
We can derive further insights from
Hires and Exits by labelling cohorts related to specific
Sources and Destinations of talent.
This analysis can be a great way to better infer quality of talent (Hires) and whether a specific company is losing talent directly to competitors (Exits).
Educational Background chart classifies granular degree information to several high-level bins. This view is most useful when analyzing companies for technical academic experience (eg. PhDs).
Educational experience tends to have a true-positive bias, as not all external profiles will list degree information.
Skills chart counts the number of unique counts of each skill tag. Each skill will only be counted once per profile.
This basic version of skills will show the top 30 skills by count and can be filtered by role segment.
Skills analysis tends to be most useful when looking at technical roles. Typical insights are related to tech stack, tool usage, and creating like-for-like analysis when there are drastic variations in titles between companies (e.g. look at count of Python vs. how many "data scientists" by job title).