HR Analytics
Your HR department has valuable data that nobody is looking at. Turnover, absenteeism, costs, performance: all of that tells stories about your organization. I help you listen to them.
What is HR Analytics?
It's using human resources data to make better decisions about people. Instead of deciding based on intuition or "we've always done it this way," you use numbers.
It's not new. Large companies have been doing it for years. What's new is that now an SME can do it without spending millions on software or hiring data scientists.
Questions HR Analytics can answer:
- → Why are people leaving? In which areas? At what tenure?
- → How much does turnover really cost us?
- → What absenteeism patterns do we have? Are there problem areas?
- → Are our salaries competitive compared to the market?
- → Do the trainings we do have measurable impact?
Metrics you can start measuring today
Turnover
- • Turnover rate by area, position, tenure
- • Voluntary vs involuntary turnover
- • Average time of permanence
- • Estimated cost of turnover
- • Exit reasons (if you track them)
Absenteeism
- • Absenteeism rate by area
- • Days lost by type (medical leave, PTO, etc.)
- • Absence patterns (days of the week, seasons)
- • Cost of absenteeism
Personnel costs
- • Cost per employee (total, not just salary)
- • Cost distribution by area
- • Overtime and its cost
- • Evolution of total payroll
Headcount composition
- • Headcount by area, contract type
- • Tenure pyramid
- • Distribution of positions and levels
- • Headcount evolution over time
How I work on HR Analytics
Available data diagnosis
We review what data you have in your ERP, payroll system, attendance tracking, etc. You don't need special HR software: most companies already have the basics in their existing systems.
Key questions definition
We don't measure everything. We define which business questions we want to answer. Do you want to understand why people leave? Optimize personnel costs? Detect areas with climate issues? That determines what we analyze.
Data cleaning and modeling
HR data is usually messy: dates entered wrong, non-standardized job titles, employees who left but are still active. We clean that before analyzing.
Analysis and visualization
We generate the analyses and present them in a useful format: it can be a Power BI dashboard, a periodic report, or a one-time analysis. Depends on what you need.
Action plan
An analysis without actions is useless. We define what we're going to do with what we found. If we discover turnover is concentrated in one area, what do we do about it?
A real case
The problem
Management perceived turnover was "high," but they didn't have clear numbers. HR spent time recruiting replacements but nobody analyzed why people were leaving.
What we found
- → 40% of resignations happened in the first 90 days of employment
- → Those who passed 90 days stayed an average of 2.5 years
- → The annual cost of those early departures was ~$100,000 USD
- → The problem wasn't salary or the role: it was the onboarding process
What they did
They redesigned onboarding: assigned buddies, structured the first week, and set up follow-up meetings at one month and 60 days.
Result
Turnover in the first 90 days dropped by half. Turnover costs were reduced by about $50,000 USD per year. The analysis cost a fraction of that.
Frequently asked questions
Do I need special HR Analytics software?
Not to start. If you already have a payroll system and attendance tracking, you have the basic data. With Power BI or even well-used Excel, you can do a lot.
What company size is needed for this to make sense?
From about 50 employees you can start seeing interesting patterns. Less than that, the numbers are too small for statistical conclusions. But even in small companies, understanding your turnover cost can be revealing.
Is my employee data secure?
I sign confidentiality agreements before starting. Data doesn't leave your environment: I work in your infrastructure or files you control. Analyses are anonymized when necessary.
Can you predict who will resign?
Yes, it's possible to build turnover prediction models. But honestly, for most SMEs, it's better to start by describing well what's happening before trying to predict. Predictive models require more data and more analytical maturity.
Want to start using HR data?
We can start with something simple: a turnover and absenteeism analysis with the data you already have. In one or two weeks you have your first report.