In a modern company, working with people has long gone beyond intuition and personal experience. For the team to feel comfortable and grow with the business, decisions must be based on facts and evidence. We have gradually built a People Analytics system — an HR data analysis approach that helps not only count numbers but also understand what lies behind them and how to improve processes for the better.
HR Metrics We Collect
Our work with data starts with regular monitoring of key indicators.
1. Employee turnover
We track the percentage of employees who leave the company over a specific period. These numbers indicate how comfortable people feel working with us and which departments require additional support.
2. Employee satisfaction
Once a quarter, we conduct anonymous surveys in which employees evaluate the work atmosphere, transparency of communication, quality of management, and work-life balance.
3. Time to fill a vacancy
We track the number of days that pass from the opening of a position to a new employee’s start date. This enables us to evaluate the effectiveness of recruiting and pinpoint areas for improvement in the hiring process.
4. Hiring cost
We collect information on expenses related to attracting candidates, including job advertisements, recruiter services, and interviewer time.
5. Onboarding indicators
We monitor how new hires pass their probation period, how quickly they adapt to tasks, and when they achieve their first results.
How We Analyze the Data
Collecting metrics is only half the job. The key is proper analysis.
We use several tools:
- a specialized HR system for storing and visualizing data
- regular dashboard reports for department heads
- HR team meetings to discuss trends and potential risks
We compare data by quarter, year, and between teams. We seek correlations, such as the relationship between employee satisfaction and hiring speed, or the effect of policy changes on retention rates.
Data-Driven Decisions in Action
1. New hiring approach
A year ago, we noticed a troubling trend: the time to fill technical positions had increased by almost 40 percent. Analysis revealed that the primary barrier was a lengthy, multi-stage interview process and an excessively long test assignment. We reduced the number of interviews from four to two and shortened the test. As a result, the average hiring time dropped from 62 to 35 days without a loss in candidate quality.
2. Employee retention program
Turnover data revealed a high rate of resignations within the first six months. An additional survey of new hires showed a lack of support after onboarding. We introduced a mentorship program and regular HR check-ins during the first half of the year. Two quarters later, the early attrition rate fell by 25 percent.
3. Improved onboarding
Surveys of new employees indicated that the first weeks felt overwhelming. We redesigned the adaptation program by splitting training into shorter modules, adding interactive tasks, and making the schedule more flexible. After these changes, the new hire satisfaction index rose from 74 to 88 percent.
4. Bonus system adjustment
Feedback from anonymous surveys indicated that employees did not fully understand the principles governing bonus distribution. We held a series of meetings to explain evaluation criteria in detail and updated internal documents. In the next survey, trust in the reward system increased by 20 percent.
People Analytics Is More Than Numbers
Working with HR metrics is often perceived as a dry set of statistics. In reality, it is a tool for understanding the team’s actual needs.
Key effects we observe:
- Transparency. When employees know that data analysis is used to improve processes rather than control them, trust grows.
- Predictability. Data analysis enables the detection of potential risks in advance, such as rising turnover or team overload.
- Collaboration. Department heads start to see HR as a partner rather than just an administrative function.
- Personal engagement. Employees feel their opinions matter because their responses directly influence decisions.
How We Ensure Data Quality
For People Analytics to be effective, information must be accurate and reliable.
- Regularity. We record metrics monthly and conduct key surveys quarterly.
- Anonymity. All surveys are fully confidential, which encourages honest responses.
- Unified standards. All departments use the same criteria for calculating indicators so that data can be compared.
- Feedback. After each survey, we share results and action plans. This builds trust in the process and motivates participation.
Results Achieved
After two years of working with People Analytics, we have seen clear results:
- The average time to fill a vacancy has dropped by 30 percent.
- Employee satisfaction consistently stays above 85 percent.
- The number of resignations within the first six months has decreased by a quarter.
- Department heads are now initiating joint projects with HR more frequently to improve working conditions.
Plans for the Future
We continue to develop People Analytics. In the coming months, we plan to:
- introduce predictive models to assess the risk of employee turnover
- Expand engagement surveys with questions about corporate culture.
- integrate HR data with performance metrics to better see the link between motivation and results
Conclusion
HR data is more than tables and charts. It is a way to hear the people who make the company successful. Analyzing metrics helps us spot weak points, respond quickly to changes, and create an environment where employees feel valued and heard.
People Analytics has become a tool that connects business goals with human needs. Thanks to this approach, the company continues to grow steadily, and the team develops with confidence and satisfaction.
Contact information
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