Lexique RH

People Analytics: Definition, Use Cases and Implementation

What is people analytics? Definition, key indicators, concrete use cases and guide to deploying HR analytics in your organisation.

People Analytics: Definition, Use Cases and Implementation

Sommaire

56%
Companies using people analytics
+25%
Improvement in recruitment quality
-30%
Turnover reduction via predictive analytics
4x
Average ROI of a people analytics project

Definition

People analytics (or HR analytics) is systematic use of employee data to make informed HR decisions. It goes beyond simple reporting (descriptive dashboards) by integrating predictive analysis (identify departure risk), prescriptive (recommend actions) and causal (understand engagement drivers). It's the shift from intuitive to data-driven HR.

The 4 Maturity Levels in People Analytics

Maturity Matrix

LevelAnalysis TypeQuestion AskedExample
1. DescriptiveReporting, dashboardsWhat happened?Our turnover is 15% this year
2. DiagnosticRoot cause analysisWhy did it happen?Turnover concentrated on tech profiles < 2 years
3. PredictiveStatistical models, MLWhat will happen?3 senior developers have 70% departure probability in 6 months
4. PrescriptiveAction recommendationsWhat should we do?Increase 8% and offer 3-day remote to retain these profiles

Concrete Use Cases in Recruitment and HR

  1. 1

    Optimise sourcing channels

    Analyse cost per hire and recruitment quality by channel (job boards, referral, direct sourcing) to reallocate budget to top performers.

  2. 2

    Predict departure risks

    Cross engagement, tenure, promotion and salary data to identify at-risk staff and act preventively.

  3. 3

    Reduce recruitment bias

    Analyse conversion rates per step by demographics to detect and correct systematic bias.

  4. 4

    Measure training ROI

    Correlate training investment with performance, mobility and retention to prioritise high-impact programmes.

  5. 5

    Plan staffing (workforce planning)

    Model foreseeable departures and future needs to anticipate recruitment 12 to 24 months ahead.

Prerequisites for people analytics

  • Reliable, centralised data

    Updated HRIS, recruitment data (ATS), engagement surveys

  • Key indicators defined

    Turnover, time-to-hire, cost per hire, employee NPS, absenteeism

  • Data skills available

    An HR analyst or data analyst (even part-time)

  • Data governance

    GDPR compliance, anonymisation, ethics committee for predictive analytics

  • CODIR sponsor

    People analytics needs leadership support for data access and resources

0/5 effectué(s)0%

GDPR Attention

People analytics uses sensitive personal data. Strictly comply with GDPR: informed consent, legitimate purpose, data minimisation, access rights. Predictive analytics on individuals (departure risk score) requires particular care: prioritise aggregated analysis and consult your DPO.
Do you need a data scientist for people analytics?
Not necessarily to start. Level 1 (descriptive reporting) is achievable with a spreadsheet and well-configured HRIS. For predictive analysis (levels 3-4), data skill is needed, but can be shared with other departments or outsourced occasionally.
Which HR KPIs to prioritise?
Start with fundamentals: turnover (global and by segment), time-to-hire, cost per hire, trial period validation rate, absenteeism and employee NPS. These 6 cover major retention, recruitment and engagement challenges.
Does people analytics replace HR intuition?
No, it complements and validates it. An experienced HR Director's intuition remains valuable, but data lets you validate it, spot blind spots and justify decisions to leadership. Goal is data-informed decision-making, not HR dehumanisation.

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