基本简介

根据美国数据库营销研究所Arthur Hughes的研究，客户数据库中有3个神奇的要素，这3个要素构成了数据分析最好的指标：

最近一次消费 (Recency)

消费频率 (Frequency)

消费金额 (Monetary)

–来源百度百科

定义

Recency：最近一次消费，意指上一次购买距离现在的时间，一般按照天进行计算

Frequency：消费频率是顾客在限定的期间内所购买的次数，时间范围需要自己设定

Monetary：指的是一段时间（通常是1年）内的消费金额

实例：

#定义相关函数
#三个指标的分值函数
def Recency(x):if x>=0 and x<5:return 5elif x>=5 and x<15:return 4elif x>=15 and x<30:return 3elif x>=30 and x<90:return 2elif x>=90:return 1def Frequency(x):if x>=0 and x<10:return 1elif x>=10 and x<30:return 2elif x>=30 and x<50:return 3elif x>=50 and x<100:return 4elif x>=100:return 5def Monetory(x):if x>=0 and x<300:return 1elif x>=300 and x<1000:return 2elif x>=1000 and x<3000:return 3elif x>=3000 and x<10000:return 4elif x>=1000:return 5#判断函数
def judge(x):if x>avg:return 1else:return 0#定义RFM函数
def RFM(x):if x.iloc[0]==1 and x.iloc[1]==1 and x.iloc[2]==1:return "重要价值客户"elif x.iloc[0]==1 and x.iloc[1]==1 and x.iloc[2]==0:return "重要潜力客户"elif x.iloc[0]==1 and x.iloc[1]==0 and x.iloc[2]==1:return "重要发展客户"elif x.iloc[0]==1 and x.iloc[1]==0 and x.iloc[2]==0:return "新客户"elif x.iloc[0]==0 and x.iloc[1]==1 and x.iloc[2]==1:return "重要保持客户"elif x.iloc[0]==0 and x.iloc[1]==1 and x.iloc[2]==0:return "一般客户"elif x.iloc[0]==0 and x.iloc[1]==0 and x.iloc[2]==1:return "重要挽回客户"elif x.iloc[0]==0 and x.iloc[1]==0 and x.iloc[2]==0:return "流失客户"

#分值转化
data["R-SCORE"] = data["R"].apply(Recency)
data["F-SCORE"] = data["F"].apply(Frequency)
data["M-SCORE"] = data["M"].apply(Monetory)#查看分组情况，用来查看分组的分布是否合理
grouped_r=data["R-SCORE"].groupby(data["R-SCORE"]).count()
grouped_f=data["F-SCORE"].groupby(data["F-SCORE"]).count()
grouped_m=data["M-SCORE"].groupby(data["M-SCORE"]).count()#计算查看平均值
avg_r = data["R-SCORE"].mean()
avg_f = data["F-SCORE"].mean()
avg_m = data["M-SCORE"].mean()
display(avg_r,avg_f,avg_m)#也可以用pandas_profiling进行数据预览
import pandas_profiling
pandas_profiling.ProfileReport(data)#0-1分类
for avg in [avg_r,avg_f,avg_m]:if avg==avg_r:data["R-SCORE是否大于均值"] = data["R-SCORE"].apply(judge)elif avg==avg_f:data["F-SCORE是否大于均值"] = data["F-SCORE"].apply(judge)elif avg==avg_m:data["M-SCORE是否大于均值"] = data["M-SCORE"].apply(judge)#打标签
data["RFM"] = data[["R-SCORE是否大于均值","F-SCORE是否大于均值","M-SCORE是否大于均值"]].apply(RFM,axis=1)
data.head()

#数据汇总
result = data.groupby("RFM").agg({"RFM":"count"})#数据展示
from pyecharts import options as opts
from pyecharts.charts import TreeMapdata = [{"value": int(result['RFM'][0]), "name": result['RFM'].index[0]},{"value": int(result['RFM'][1]), "name": result['RFM'].index[1]},{"value": int(result['RFM'][2]), "name": result['RFM'].index[2]},{"value": int(result['RFM'][3]), "name": result['RFM'].index[3]},{"value": int(result['RFM'][4]), "name": result['RFM'].index[4]},{"value": int(result['RFM'][5]), "name": result['RFM'].index[5]},{"value": int(result['RFM'][6]), "name": result['RFM'].index[6]},{"value": int(result['RFM'][7]), "name": result['RFM'].index[7]},
]c = (TreeMap().add("RFM模型", data,label_opts= opts.LabelOpts(font_size= 12,position = "inside")).set_global_opts(title_opts=opts.TitleOpts(title=""))
)   
c.render_notebook()