用戶分層-RFM

基本簡介

根據美國數據庫營銷研究所Arthur Hughes的研究，客戶數據庫中有3個神奇的要素，這3個要素構成了數據分析最好的指標：

最近一次消費 (Recency)

消費頻率 (Frequency)

消費金額 (Monetary)

–來源百度百科

定義

Recency：最近一次消費，意指上一次購買距離現在的時間，一般按照天進行計算

Frequency：消費頻率是顧客在限定的期間內所購買的次數，時間範圍需要自己設定

Monetary：指的是一段時間（通常是1年）內的消費金額

實例：

#定義相關函數
#三個指標的分值函數
def Recency(x):
    if x>=0 and x<5:
        return 5
    elif x>=5 and x<15:
        return 4
    elif x>=15 and x<30:
        return 3
    elif x>=30 and x<90:
        return 2
    elif x>=90:
        return 1
    
def Frequency(x):
    if x>=0 and x<10:
        return 1
    elif x>=10 and x<30:
        return 2
    elif x>=30 and x<50:
        return 3
    elif x>=50 and x<100:
        return 4
    elif x>=100:
        return 5
    
def Monetory(x):
    if x>=0 and x<300:
        return 1
    elif x>=300 and x<1000:
        return 2
    elif x>=1000 and x<3000:
        return 3
    elif x>=3000 and x<10000:
        return 4
    elif x>=1000:
        return 5
   

#判斷函數
def judge(x):
    if x>avg:
        return 1
    else:
        return 0


#定義RFM函數
def RFM(x):
    if x.iloc[0]==1 and x.iloc[1]==1 and x.iloc[2]==1:
        return "重要價值客戶"
    elif x.iloc[0]==1 and x.iloc[1]==1 and x.iloc[2]==0:
        return "重要潛力客戶"
    elif x.iloc[0]==1 and x.iloc[1]==0 and x.iloc[2]==1:
        return "重要發展客戶"
    elif x.iloc[0]==1 and x.iloc[1]==0 and x.iloc[2]==0:
        return "新客戶"
    elif x.iloc[0]==0 and x.iloc[1]==1 and x.iloc[2]==1:
        return "重要保持客戶"
    elif x.iloc[0]==0 and x.iloc[1]==1 and x.iloc[2]==0:
        return "一般客戶"
    elif x.iloc[0]==0 and x.iloc[1]==0 and x.iloc[2]==1:
        return "重要挽回客戶"
    elif x.iloc[0]==0 and x.iloc[1]==0 and x.iloc[2]==0:
        return "流失客戶"
        

#分值轉化
data["R-SCORE"] = data["R"].apply(Recency)
data["F-SCORE"] = data["F"].apply(Frequency)
data["M-SCORE"] = data["M"].apply(Monetory)

#查看分組情況，用來查看分組的分佈是否合理
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()

#計算查看平均值
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進行數據預覽
import pandas_profiling
pandas_profiling.ProfileReport(data)


#0-1分類
for avg in [avg_r,avg_f,avg_m]:
    if avg==avg_r:
        data["R-SCORE是否大於均值"] = data["R-SCORE"].apply(judge)
    elif avg==avg_f:
        data["F-SCORE是否大於均值"] = data["F-SCORE"].apply(judge)
    elif avg==avg_m:
        data["M-SCORE是否大於均值"] = data["M-SCORE"].apply(judge)

#打標籤
data["RFM"] = data[["R-SCORE是否大於均值","F-SCORE是否大於均值","M-SCORE是否大於均值"]].apply(RFM,axis=1)
data.head()

#數據彙總
result = data.groupby("RFM").agg({"RFM":"count"})

#數據展示
from pyecharts import options as opts
from pyecharts.charts import TreeMap

data = [
    {"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()