Auto Insights can help HR teams automate retention analysis, investigate employee turnover and identify attrition risks. You can read more here on our website.
This article will cover:
Example insights from this use case
Recommended data structure for this use case
What sort of insights can Auto Insights help me uncover?
We've outlined some example questions which Auto Insights can help answer through a combination of its proactive insights, Search, and What caused this? analysis:
Automate retention analysis
Average leaving probability by team
Show me terminations by department last quarter
Average years of service at termination by department
Number of terminations by department and office location
Investigate employee turnover
Average years of service by department
Number of terminations by rank and department
Leaving probability by rank and team
Quarter on quarter terminations by rank and department
Identify attrition risks
Number of employees by years of service and position
Leaving probability by rank last six months
Percentage growth of leaving probability by team this quarter
How do I structure my data?
Auto Insights requires structured, transactional data, with at least 1 measure (e.g. Years of service) and 5 segments (e.g. Employee rank). In addition, we recommend at least 7 months of data (at monthly or daily granularity) so you can take full advantage of Auto Insights' Unexpected Changes feature.
Example data structure
Please refer to this article for more information about data structure.
Here are some of the typical segments we find in HR data. A segment is a qualitative value, like names or categories:
Employee attributes: Employee name, Manager name, employee rank, employee team name, employee department name, hire date, termination date, employee ID, employee age, employee gender, employee location, employment type etc.
A measure is a quantitative, numeric value. Some of the typical measures include monthly salary, years of service, leaving probability etc.
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