47 lines
1.6 KiB
Markdown
47 lines
1.6 KiB
Markdown
---
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title: "RFM Analysis"
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type: concept
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tags: []
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last_updated: 2026-04-21
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---
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## Definition
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客户价值分析的经典方法，通过三个维度评估客户：Recency（最近购买时间）、Frequency（购买频率）、Monetary（消费金额）。
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## Scoring Method
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- R Score：最近购买距离当前天数，越小分数越高（1-5 分）
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- F Score：购买次数排名分位数（1-5 分）
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- M Score：消费总额分位数（1-5 分）
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- RFM Score：三个分数组合形成 555-111 的客户评分
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## Customer Segments
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| RFM Score | Segment | Strategy |
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|-----------|---------|----------|
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| 555, 554, 544, 545, 454, 455, 445 | Champions | 奖励忠诚度，请求推荐，升级 |
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| 543, 444, 435, 355, 354, 345, 344, 335 | Loyal Customers | 培育关系，推荐新产品，会员计划 |
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| 553, 551, 552, 541, 542, 533, 532, 531, 452, 451 | Potential Loyalists | 升级优惠，交叉销售 |
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| 512, 511, 422, 421, 412, 411, 311 | New Customers | 优化入职，早期互动 |
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| 155, 154, 144, 214, 215, 115, 114 | At Risk | 再激活活动，特别优惠 |
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## Implementation
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```python
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# RFM Analysis Python Implementation
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rfm = df.groupby('customer_id').agg({
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'date': lambda x: (current_date - x.max()).days, # Recency
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'order_id': 'count', # Frequency
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'revenue': 'sum' # Monetary
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}).rename(columns={
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'date': 'recency',
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'order_id': 'frequency',
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'revenue': 'monetary'
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})
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```
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## Related Concepts
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- [[Customer Lifetime Value]]
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- [[Data-Driven Decision Making]]
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## Source
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- [[support-analytics-reporter]]
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