RFM客户价值分类
作者：互联网
1 # 自定义好的包,亲测可用 原数据和代码思想来自以下网址
2 # https://github.com/joaolcorreia/RFM-analysis
3 import datetime as dt
4 import pandas as pd
5 def RFM(data_,ri_qi,user_id,money,*time_):
6 '''
7 return:
8 dataframe表格
9 列有r,f,m,r_四分位分数,f_四分位分数,m_四分位分数,RFM,用户分类
10 行为用户id,唯一,分类汇总后
11 desc:
12 如 : RFM(orders,'order_date','customer','grand_total',2018,1,2)
13 返回df数据会复制到粘贴板
14 params:
15 data_ : 表格数据 pandas dataframe (表格中的字段名)
16 ri_qi : 订单日期 (表格中的字段名)
17 user_id : 用户id (表格中的字段名)
18 money : 消费金额 (表格中的字段名)
19 *time_ : 不定长参数 不写默认当天
20
21 '''
22
23 # 设置参考时间点,无参数则默认为当天
24 if time_ == ():
25 NOW = dt.datetime.now()
26 else:
27 y,m,d = time_
28 NOW = dt.datetime(y,m,d)
29
30 # 日期列进行格式转换
31 data_[ri_qi] = pd.to_datetime(data_[ri_qi])
32
33 # 创建RFM标签
34 rfmTable = data_.groupby(user_id).agg({ri_qi: lambda x: (NOW - x.max()).days, # Recency 顾客最近一次购买距今天数(days)
35 'order_id': lambda x: len(x), # Frequency 按顾客分组后次数,即顾客订单频次
36 money: lambda x: x.sum()}) # Monetary Value 顾客消费金额总和
37 # 日期列转换数据类型
38 rfmTable[ri_qi] = rfmTable[ri_qi].astype(int)
39 # 列名重命名
40 rfmTable.rename(columns={ri_qi: 'recency',
41 'order_id': 'frequency',
42 money: 'monetary_value'}, inplace=True)
43
44 # 确定RFM四分位数
45 quantiles = rfmTable.quantile(q=[0.25,0.5,0.75])
46 quantiles = quantiles.to_dict()
47
48 # 创建RFM分割表
49 rfmSegmentation = rfmTable.copy(deep=True)
50 # Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)
51 # 日期间隔越小越好
52 def RClass(x,p,d):
53 # p,d = args = ('recency',quantiles,)
54 # x即rfmSegmentation['recency'].apply(RClass) 日期间隔数据
55 if x <= d[p][0.25]:
56 return 1
57 elif x <= d[p][0.50]:
58 return 2
59 elif x <= d[p][0.75]:
60 return 3
61 else:
62 return 4
63
64 # Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)
65 # 消费频次和金额越大越好
66 # 分数范围1-4
67 def FMClass(x,p,d):
68 if x <= d[p][0.25]:
69 return 4
70 elif x <= d[p][0.50]:
71 return 3
72 elif x <= d[p][0.75]:
73 return 2
74 else:
75 return 1
76
77
78 # 评分应用 R F M
79 rfmSegmentation['R_Quartile'] = rfmSegmentation['recency'].apply(RClass, args=('recency',quantiles,))
80 rfmSegmentation['F_Quartile'] = rfmSegmentation['frequency'].apply(FMClass, args=('frequency',quantiles,))
81 rfmSegmentation['M_Quartile'] = rfmSegmentation['monetary_value'].apply(FMClass, args=('monetary_value',quantiles,))
82
83 # 评分应用 RFMClass
84 rfmSegmentation['RFMClass'] = rfmSegmentation.R_Quartile.map(str) \
85 + rfmSegmentation.F_Quartile.map(str) \
86 + rfmSegmentation.M_Quartile.map(str)
87
88
89 def type_(x,R,F,M):
90 '''
91 r,f,m即每行的r,f,m分值
92 '''
93 r,f,m = x[R],x[F],x[M]
94 if r > 2 and f > 2 and m > 2:
95 return '高价值客户'
96 elif r <= 2 and f > 2 and m > 2:
97 return '高重点保护客户'
98 elif r > 2 and f <= 2 and m > 2:
99 return '重点发展客户'
100 elif r <= 2 and f <= 2 and m > 2:
101 return '重点挽留客户'
102 elif r > 2 and f > 2 and m <= 2:
103 return '一般价值客户'
104 elif r <= 2 and f > 2 and m <= 2:
105 return '一般保持客户'
106 elif r > 2 and f <= 2 and m <= 2:
107 return '一般发展客户'
108 elif r <= 2 and f <= 2 and m <= 2:
109 return '潜在客户'
110 else:
111 return
112
113 rfmSegmentation['type_'] = rfmSegmentation.apply(type_,args=('R_Quartile','F_Quartile','M_Quartile'),axis=1)
114 rfmSegmentation.to_clipboard() # 数据复制到 粘贴板
115 return rfmSegmentation
调用函数处理后:
标签：rfmTable,return,分类,id,客户,qi,ri,RFM 来源： https://www.cnblogs.com/mschen/p/11628394.html