Brazil has the highest turnover in the world. Your company manages it with a spreadsheet.
Kavuka People Analytics unifies payroll, time, ATS and performance into a single base and adds what no one else has — the psychometric behavioral layer and the platform’s predictive engine — to deliver per-person and per-team attrition risk, explained factor by factor, with time to act.
- Per person
- attrition-risk score
- Single base
- payroll, time, ATS and performance
- Measured
- quality of every hire
- By design
- documented governance
Single base consolidating payroll, time, ATS, performance and psychometrics across high-volume operations — thousands of employees monitored by attrition-risk score, with data-protection governance by default.
Every year half of your team turns over — and the turnover report only describes the damage after it happens.
The exit nobody saw coming
People data scattered across payroll, time, ATS and spreadsheets that don’t talk — and the key talent resigns before any alert.
The replacement cost invisible in the P&L
Each exit costs 50% to 200% of the annual salary in recruiting, training and lost productivity — an entire acquisition budget burned to reinstall what was already there.
HR held to strategy, armed with a spreadsheet
People decisions made by intuition in the country with the world’s highest turnover — and HR at the planning table without the numbers to back the conversation.
Cost Brazil leads the world turnover ranking — up 56% over the pre-pandemic period (Robert Half, on CAGED data), with one in every two employees replaced per year. The cost exceeds R$ 600 billion a year in labor expenses and lost productivity (Sólides), and nearly 4 in 10 exits hit professionals rated as qualified. How much of that is your company paying without seeing it?
From raw data to retention by method, in one cycle.
- 01
Connect
Payroll, time, ATS and performance integrated into a single base — the metrics that matter, clean and comparable, without replacing your HR systems.
- 02
Enrich
The psychometric behavioral layer (Behavioral Analysis and GUÉP Human Score) and the verified context (Workforce Screening) added to transactional data.
- 03
Predict
The predictive engine scores attrition risk per person and per team, with explainable factors — relative pay, stagnation, overload, time patterns and profile — and time to act.
- 04
Act and measure
Retention plans with measured effect and the quality of every hire tracked from funnel to tenure — the closed loop between hiring, performance and turnover.
The engine behind every talent decision
A single base cross-references internal HR data with the platform’s exclusive layers and returns metrics, scores and scenarios — ready to decide with evidence instead of intuition.
Single people base
Payroll, time, ATS and performance connected
Indicator diagnosis
Turnover by segment, tenure, absenteeism
Attrition prediction
Resignation-risk score per person and team
Explainable factors
Pay, stagnation, overload and patterns
Quality of hire
Hiring → performance → tenure link
Behavioral layer
Big Five, DISC and GUÉP Human Score by team
Team composition
Predictable chemistry and friction at scale
Workforce planning
Headcount, cost and succession scenarios
Who decides with Kavuka People Analytics
Retail, Logistics & BPO
Operations where turnover exceeds 80% and each point of churn is worth millions — attrition prediction on the front line.
Technology & Knowledge
Retention of critical talent: the attrition score in the right 1:1, at the right time, before the resignation.
Industry
A shortage of qualified labor demands workforce and succession planning with numbers — not intuition.
Groups & Chains
Internal benchmarking: why unit A retains and unit B bleeds — comparable, team by team.
People analysis with governance, not risk
Analyzing people data without governance becomes a liability: discriminatory risk in the model, individual analysis without transparency, undefined purpose. Kavuka People Analytics was designed for data-protection law from the base — governance is not an annex, it is how the analysis operates.
- Documented legitimate purpose for every processing of people data, with an adequate legal basis.
- Transparency with data subjects and data minimization: only what is necessary for the stated purpose.
- Aggregation by default in dashboards; individual analysis restricted to those who need it, with an access trail.
- Bias mitigation validated in the predictive models — a score that explains, not one that discriminates.
- Data Processing Agreement available for enterprise clients; encryption in transit and at rest.
We saw three senior engineers leaving 60 days in advance. Two stayed. The avoided cost paid for the platform in the first quarter.
For the first time we sat in the committee with the real cost of turnover, team by team. The conversation stopped being intuition and became a plan.
We discovered the unit that retained best had a replicable hiring pattern. We took it to the others and churn dropped double digits.
Discover the real cost of your turnover — and who is 90 days from leaving.
In 15 minutes you see the single base and the attrition-risk score running on your scenario, team by team.
- For businesses only. No purchase commitment.
- Data used solely for commercial contact.
- Enterprise leads answered within 1 business day.
What people analytics is and how to apply it
People analytics is the discipline of deciding about people with data: measuring, understanding and predicting workforce phenomena — turnover, performance, engagement, absenteeism, composition and cost — to turn people management from reactive into predictive. Instead of describing the past in a turnover report, the discipline answers the questions that matter: who is at risk of leaving, why, and with how much time to act? Which hires work out, and what do the professionals who stay and perform have in common? Where is the company losing money to churn that could be prevented?
The context that makes this discipline urgent in Brazil is brutal: the country leads the world turnover ranking, up 56% over the pre-pandemic period (Robert Half, on CAGED data) and with annual rates around 50% — one in every two employees replaced per year. Replacement costs 50% to 200% of the annual salary per exit, adding up to more than R$ 600 billion a year in labor expenses and lost productivity. And nearly 4 in 10 exits hit professionals rated as qualified: it is not just volume, it is the loss of those already delivering. In such a scenario, managing people by intuition is the most expensive luxury there is.
Applying real people analytics means going beyond the dashboard, which has become a commodity in the global suites (Visier, Workday, SAP SuccessFactors) and the Brazilian management platforms (Sólides, Gupy and peers). The common structural weakness of these tools is analytics locked to internal HR data — without a deep behavioral layer, without verified context and without a real predictive engine. The Kavuka difference is people analytics that combines transactional HR data with the three layers only the platform has: the behavioral profile from psychometrics (Behavioral Analysis with the GUÉP Human Score), the verified context from the Workforce Screening pipeline, and the Risk Scoring engine applied to people — attrition risk as an explainable score, not a surprise.
People analytics and data-protection law are not opposites: the analysis relies on documented legitimate purpose, transparency with data subjects, data minimization and aggregation by default in dashboards, with individual analysis restricted to those who need it and bias mitigation validated in the models. The result is a closed loop — connect the single base, enrich it with the behavioral layer and verified context, predict attrition risk per person and per team, and act with retention plans of measured effect. It is retention by method instead of replacement by habit; hiring that learns from its own history; and HR at the planning table, with headcount, cost and succession scenarios backed by numbers.
What is people analytics?
It is the application of data and models to people management: measuring workforce phenomena (turnover, performance, engagement, cost), understanding their causes and predicting their movements — to decide with evidence instead of intuition.
How does attrition prediction work?
The model learns from the company’s own history — who left, with what pattern of signals — and scores current risk per person and per team, with explainable factors (relative pay, stagnation, overload, time patterns, behavioral profile). The score arrives with time to act, not as an autopsy.
Is this compatible with data-protection law?
Yes, with governance designed in: documented legitimate purpose, transparency with data subjects, minimization, aggregation by default in dashboards and individual analysis restricted to those who need it — plus bias mitigation validated in the models.
Do I need to replace my HR systems?
No. People Analytics connects existing systems (payroll, time, ATS, performance) via integrations and consolidates the single base on top of them, without disruption.
What does the behavioral layer add?
The dimension transactional data lacks: the profile (Big Five, DISC and the GUÉP Human Score synthesis) explaining fit, team chemistry and tenure patterns — the psychometrics of Behavioral Analysis feeding the prediction.
How do you measure the quality of a hire?
By the hiring → performance → tenure link: not by filling the role, but by what the hires who stay and perform have in common. The platform bridges with KYA and Behavioral Analysis to close that loop.
What is the difference between people analytics and an HR dashboard?
The dashboard describes the past; people analytics predicts the future. The dashboard has become a commodity — explainable prediction is the product. The Kavuka difference is adding three exclusive layers to analytics: psychometrics, verified context and a real predictive engine.
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