HR data and workforce analytics analysis
Complete HR data analysis with turnover prediction, training ROI, and talent management recommendations.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Je dois analyser les données RH de [ORGANISATION] ([NOMBRE_EMPLOYES] employés) pour [PERIODE]. Données disponibles : [DONNEES_DISPONIBLES] (ex : effectifs, absences, turnover, formations, performance reviews, ancienneté, salaires, recrutement).
Objectif de l'analyse : [OBJECTIF_RH] (ex : réduire le turnover, anticiper les besoins en recrutement, mesurer l'efficacité des formations)
Réalise une analyse workforce complète :
- Tableau de bord des effectifs : répartition par département, ancienneté, genre, type de contrat
- Analyse du turnover : taux par département/période/profil avec identification des facteurs de risque
- Modèle prédictif du risque de départ : quels collaborateurs ont le plus fort risque de quitter dans les 6 mois
- Analyse des absences : taux, durées, causes, patterns temporels et corrélation avec l'engagement
- ROI des formations : corrélation entre heures de formation et performance/rétention
- Analyse des écarts de rémunération par profil (genre, ancienneté, département)
- Pyramide des âges et anticipation des départs en retraite sur 5 ans
- Recommandations prioritaires pour [OBJECTIF_RH] avec indicateurs de suivi
Conformité RGPD : précise les données à anonymiser.
Outil : [OUTIL] (Excel, Power BI, Python)
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Why this prompt works
<p>This prompt is effective because it integrates both descriptive analysis and a predictive HR churn model, moving from observation to anticipation with an important mention of GDPR compliance.</p>
Use Cases
Expected Output
Complete HR dashboard, predictive turnover model, training ROI analysis, age pyramid, and recommendations.
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- LéaAI
Pour un modèle prédictif fiable, commencez par nettoyer les données : repérez les valeurs manquantes et les outliers sur l’ancienneté et les salaires. Sans cette étape, le risque d’erreur augmente. Utilisez une validation croisée pour éviter le surapprentissage, surtout si vous avez peu d’employés.
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