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✍️RedactionIntermediateAll AIs

Structured Client Case Study

Creates a complete client case study with Challenge/Solution/Result structure, before/after metrics, and featured testimonial.

Paste in your AI

Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.

Rédige une étude de cas client complète et convaincante.

Nom du client (ou pseudonyme) : [NOM DU CLIENT]
Secteur du client : [SECTEUR]
Taille de l'entreprise : [TAILLE : TPE / PME / ETI / Grand groupe]
Défi rencontré avant de travailler avec nous : [PROBLÈME INITIAL]
Solution mise en place : [SOLUTION / PRODUIT / SERVICE FOURNI]
Durée de la collaboration : [DURÉE]
Résultats obtenus (chiffrés) : [RÉSULTAT 1], [RÉSULTAT 2], [RÉSULTAT 3]
Citation du client : [NOM], [TITRE], [ENTREPRISE]

Structure de l'étude de cas :

  1. Titre accrocheur avec le résultat principal chiffré
  2. Résumé en 3 bullets (Défi / Solution / Résultat)
  3. CONTEXTE : Présentation du client et de son secteur
  4. LE DÉFI : Problème en détail, impact sur le business
  5. POURQUOI NOUS : Processus de sélection, critères de choix
  6. LA SOLUTION : Ce qui a été mis en place, étapes clés
  7. LES RÉSULTATS : Métriques avant/après, tableau comparatif
  8. CITATION TÉMOIGNAGE : Mise en avant visuelle
  9. ET APRÈS : Prochaines étapes de la collaboration
  10. CTA : Comment obtenir des résultats similaires

Ton : factuel, orienté résultats, humble
Longueur : 600-900 mots

Personalize this prompt with Léa

Léa rewrites this prompt for your job and your exact goal — 3 quick questions.

Why this prompt works

The Why Us section is often overlooked but crucial: it reassures prospects by showing that others conducted a rigorous selection process before choosing this provider, reducing perceived risk.

Use Cases

Sales teams seeking social proofCorporate websites for the references sectionClient presentations and RFP responses

Expected Output

A 600-900 word case study with 10-section structure, before/after table, testimonial, and call to action.

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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.

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Comments

  • LéaAI

    Remplissez d'abord chaque crochet avec des données réelles précises (chiffres, durées, noms) : cela transforme le prompt en un générateur fiable d’étude de cas, sans hallucination ni brouillon flou.

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