Segmentation client
PROSegmente les clients avec l'analyse RFM, le clustering K-Means et les données comportementales pour identifier les groupes à haute valeur, prédire le churn et construire des stratégies d'acquisition ciblées.
Exemple d'Utilisation
J’aimerais de l’aide pour segmenter ma base clients.
Prompt du Skill
Ce skill fonctionne mieux lorsqu'il est copié depuis findskill.ai — il inclut des variables et un formatage qui pourraient ne pas être transférés correctement ailleurs.
Comment Utiliser Ce Skill
1
Copier le skill avec le bouton ci-dessus
2
Coller dans votre assistant IA (Claude, ChatGPT, etc.)
3
Remplissez vos informations ci-dessous (optionnel) et copiez pour inclure avec votre prompt
4
Envoyez et commencez à discuter avec votre IA
Personnalisation Suggérée
| Description | Par défaut | Votre Valeur |
|---|---|---|
| Days to look back for calculating recency score (90 for fast-moving retail, 730 for B2B) | 365 | |
| Number of clusters for K-Means algorithm (determine via Elbow Method, typically 3-8) | 5 | |
| Percentile threshold above which customers are considered high monetary value | 75 | |
| Days of inactivity defining a customer as churned (30 for SaaS, 365 for annual subscription) | 180 | |
| Minimum purchase frequency to classify as frequent buyer (varies by industry) | 5 | |
| Target Customer Acquisition Cost payback period in months | 12 |
Sources de Recherche
Ce skill a été créé à partir de recherches provenant de ces sources fiables :
- Comarch Customer Segmentation Guide Comprehensive 7-step segmentation strategy covering geographic, demographic, psychographic, behavioral, needs-based, and value-based approaches
- 8 Proven Customer Segmentation Frameworks Details 8 frameworks including 4A Model, 3C Framework, STP, RFM Analysis, 5W Framework, Persona Framework
- How to Perform Customer Segmentation: 5-Step Strategy Step-by-step implementation guide with real examples of demographic, geographic, psychographic, and behavioral approaches
- Ultimate Framework for Segmenting Customers Practical framework covering behavioral, psychographic, RFM segmentation across CRM, success, sales, and product teams
- Customer Segmentation in Python with Machine Learning Hands-on Python tutorial using K-Means clustering with EDA, feature scaling, and segment interpretation
- Customer Segmentation via Cluster Analysis Technical guide on K-Means, hierarchical, and density-based clustering methods
- Customer Segmentation Using K-Means Clustering Project-based tutorial covering EDA, feature transformation, elbow method, and cluster interpretation
- Machine Learning for Customer Segmentation in Retail Academic overview of ML techniques including clustering, classification algorithms, and retail case studies
- AI-driven Customer Segmentation in E-commerce Framework covering clustering algorithms, data integration, implementation case studies, and ROI measurement
- RFM Analysis Method for Customer Segmentation Detailed guide on Recency-Frequency-Monetary analysis and segment interpretation