Anna 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 Anna help me uncover?
We've outlined some example questions which Anna can help answer through a combination of her 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?
- Anna 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 Anna's 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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