고객 Segmentation
PRO고객 Segmentation 완전 정복! AI가 도와줘서 효율 200% 상승. 진짜 대박임!
사용 예시
고객 Segmentation 관련해서 조언 좀 해주세요. 뭐부터 해야 할까요?
스킬 프롬프트
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이 스킬 사용법
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스킬 복사 위의 버튼 사용
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AI 어시스턴트에 붙여넣기 (Claude, ChatGPT 등)
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아래에 정보 입력 (선택사항) 프롬프트에 포함할 내용 복사
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전송하고 대화 시작 AI와 함께
추천 맞춤 설정
| 설명 | 기본값 | 내 값 |
|---|---|---|
| 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 |
연구 출처
이 스킬은 다음 신뢰할 수 있는 출처의 연구를 바탕으로 만들어졌습니다:
- 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