原数据
一、根据RFM计算客户价值并对客户进行细分
1. 数据预处理
1.1 创建视图存储 R、F、M的最大最小值
创建视图存储R 、F、M 的最大最小值，为指标的离散提供数据
create view RFM_maxmin24(maxR,minR,maxF,minF,maxM,minM)
as
SELECT MAX(Recency) , MIN(Recency), MAX(Frequency), MIN(Frequency), MAX(Monetary), MIN(Monetary)
FROM customers1997 视图
1.2 创建视图计算对R、F、M进行离散化
注意Recency 是越小越好指标，公式同 F 和 M 有所不同
计算RFM的各项分值：
★ R ，距离当前日期越近，得分越高，最该高5分，最低1分
★ F ，交易频率越高，得分越高，最该高5分，最低1分
★ M ，交易金额越高，得分越高，最该高5分，最低1分
create view Customer_RFM
as
SELECT customer_id, Recency, Frequency, Monetary,
CASE
WHEN (maxR - Recency) <= (maxR - minR)/ 5 THEN 1
WHEN (maxR - Recency) <= 2 * (maxR - minR)/ 5 THEN 2
WHEN (maxR - Recency) <= 3 * (maxR - minR)/ 5 THEN 3
WHEN (maxR - Recency) <= 4 * (maxR - minR)/ 5 THEN 4
WHEN (maxR - Recency) <= 5 * (maxR - minR) / 5 THEN 5
ELSE NULL
END AS R,
CASE
WHEN (maxF - Frequency) <= (maxF - minF)/ 5 THEN 5
WHEN (maxF - Frequency) <= 2 * (maxF - minF) / 5 THEN 4
WHEN (maxF - Frequency) <= 3 * (maxF - minF) / 5 THEN 3
WHEN (maxF - Frequency) <= 4 * (maxF - minF) / 5 THEN 2
WHEN (maxF - Frequency) <= 5 * (maxF - minF) / 5 THEN 1
ELSE NULL
END AS F,
CASE
WHEN (maxM - Monetary) <= (maxM - minM) / 5 THEN 5
WHEN (maxM - Monetary) <= 2 * (maxM - minM)/ 5 THEN 4
WHEN (maxM - Monetary) <= 3 * (maxM - minM)/ 5 THEN 3
WHEN (maxM - Monetary) <= 4 * (maxM - minM)/ 5 THEN 2
WHEN (maxM - Monetary) <= 5 * (maxM - minM) / 5 THEN 1
ELSE NULL
END AS M
FROM customers1997 CROSS JOIN rfm_maxmin24结果：
1.3 建立客户评分表(客户行为变量表)
CREATE TABLE Customer_Value AS
SELECT customer_id, Recency, Frequency,Monetary, R, F, M,
R * 5 + F * 3 + M * 2 as value
FROM customer_rfm 
2. 细分客户价值
df_rfm = pd.read_csv("Customer_Value.csv") #相对路径读取数据# 客户细分
# 最佳客户（最有价值），常购客户，⼤额消费者，不确定客户（最不值钱）
# Top，High，Medium，Low
df_rfm['Segment'] = pd.cut(df_rfm['value'], 4, labels=['Low', 'Medium', 'High', 'Top'])
df_rfm
3. 创建气泡图，查看分布情况
# 创建⽓泡图
print("创建⽓泡图")
# 为不同的 Segment 分配颜⾊
color_map = {'Low': 'blue', 'Medium': 'green', 'High': 'orange', 'Top': 'red'}
colors = df_rfm['Segment'].map(color_map)
# 创建⽓泡图
plt.figure(figsize=(10, 6))
bubble_size = df_rfm['Recency'] * 5 # 调整⽓泡⼤⼩，以便更好的可视化
plt.scatter(df_rfm['Frequency'], df_rfm['Monetary'], s=bubble_size, c=colors, alpha=0.5)
plt.title('Customer Segmentation Bubble Chart')
plt.xlabel('Frequency (F)')
plt.ylabel('Monetary (M)')
plt.grid(True)
# 计算 Frequency 和 Monetary 的平均值
avg_frequency00 = df_rfm['Frequency'].mean()
avg_monetary00 = df_rfm['Monetary'].mean()
# 添加平均值参考线
plt.axvline(x=avg_frequency, color='black', linestyle='--', linewidth=1.5, label=f'Avg Frequency: {avg_frequency00:.2f}')
plt.axhline(y=avg_monetary, color='black', linestyle='--', linewidth=1.5, label=f'Avg Monetary: {avg_monetary00:.2f}')
# 创建图例
for segment in color_map:
plt.scatter([], [], color=color_map[segment], label=segment, alpha=0.5, s=100)
plt.legend(title='Segment', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
4. 对top用户进行分析
print("top用户分析")
top_customers = df_rfm[df_rfm['Segment'] == 'Top']
# 设置⻛格
sns.set(style="whitegrid")
# 创建可视化
plt.figure(figsize=(15, 5))
# Recency分布
plt.subplot(1, 3, 1)
sns.barplot(x='customer_id', y='Recency', data=top_customers, palette='cool')
plt.title('Top Customers Recency')
# Frequency分布
plt.subplot(1, 3, 2)
sns.barplot(x='customer_id', y='Frequency', data=top_customers, palette='cool')
plt.title('Top Customers Frequency')
# Monetary分布
plt.subplot(1, 3, 3)
sns.barplot(x='customer_id', y='Monetary', data=top_customers, palette='cool')
plt.title('Top Customers Monetary')
plt.tight_layout()
plt.show()
# 计算平均RFM值
avg_recency = top_customers['Recency'].mean()
avg_frequency = top_customers['Frequency'].mean()
avg_monetary = top_customers['Monetary'].mean()
# 输出结果
print(f"Top Average Recency: {avg_recency}")
print(f"Top Average Frequency: {avg_frequency}")
print(f"Top Average Monetary: {avg_monetary}") 
print("“Top”客户群体不仅活跃（低Recency值）⽽且⾮常忠诚（⾼Frequency值）⾼消费能⼒")
print("top人数： ")
top_counts = len(top_customers)
print(top_counts)
二、5 分法分箱（等宽/等频）对客户进行细分
分析和建模
1.“客户行为变量”表
a. 等宽
print('数据——“客户⾏为变量”表')
df #数据——“客户⾏为变量”表
# 对RFM值进⾏标准化或打分
df['R_Score'] = pd.cut(df['Recency'], 5, labels=[5, 4, 3, 2, 1])
# print(df.groupby('R_Score').R_Score.count()) # 统计各分区人数
df['F_Score'] = pd.cut(df['Frequency'], 5, labels=[1, 2, 3, 4, 5])
df['M_Score'] = pd.cut(df['Monetary'], 5, labels=[1, 2, 3, 4, 5])
# 计算RFM总分
df['RFM_Score'] = df['R_Score'].astype(int) * 5 + df['F_Score'].astype(int) * 3+ df['M_Score'].astype(int)*2b. 等频
划分的函数qcut()和等宽的cut()不一样，其他的操作都一样
print("等频")
# 利⽤等频算法将Recency划分为5个区间
df['r_discretized_2'] = pd.qcut(r, 5, labels=range(5))
print(df.groupby('r_discretized_2').r_discretized_2.count())
2. 细分客户
# 客户细分
# 最佳客户（最有价值），常购客户，⼤额消费者，不确定客户（最不值钱）
# Top，High，Medium，Low
df['Segment'] = pd.cut(df['RFM_Score'], 4, labels=['Low', 'Medium', 'High', 'Top'])
df
3. 气泡图及分析
后面的气泡图和分析同第一种情况
三、RFM数据标准化归一化（0-1）
import pandas as pd
df = pd.read_csv("customers1997.csv") #相对路径读取数据常用的规范化/标准化：
数据规范化是调整数据尺度的⼀种⽅法，以便在不同的数据集之间进⾏公平⽐较。
- 最⼤-最⼩规范化：将数据缩放到0到1之间，是⼀种常⽤的归⼀化⽅法，有助于处理那些标准化假设正态分布的⽅法不适⽤的情况。
- Z分数规范化：通过数据的标准偏差来度量数据点的标准分数，有助于数据的异常值处理和去除偏差。
- ⼩数定标规范化：通过移动数据的⼩数点位置（取决于数据的最⼤绝对值）来转换数据，使得数据更加稳定和标准化。
df_fm = df[['Frequency', 'Monetary']]
# 最⼤-最⼩规范化
scaler_minmax = MinMaxScaler()
data_minmax = scaler_minmax.fit_transform(df_fm)
# Z分数规范化
scaler_standard = StandardScaler()
data_standard = scaler_standard.fit_transform(df_fm)
# ⼩数定标规范化
df_fm = df[['Frequency', 'Monetary']]
max_vals = df_fm.abs().max()
scaling_factor = np.power(10, np.ceil(np.log10(max_vals)))
df_fm_scaled = df_fm / scaling_factor
1. 数据归一化
print('将数据归一化（0-1）')
scaler_minmax0 = MinMaxScaler()
df_rfm0 = df[['Recency','Frequency', 'Monetary']]
df_rfm0[['Recency1','Frequency1', 'Monetary1']] = scaler_minmax0.fit_transform(df_rfm0)
print("打分")
df_rfm0['R_S'] = pd.cut(df_rfm0['Recency1'], 5, labels=[5, 4, 3, 2, 1])
df_rfm0['F_S'] = pd.cut(df_rfm0['Frequency1'], 5, labels=[1, 2, 3, 4, 5])
df_rfm0['M_S'] = pd.cut(df_rfm0['Monetary1'], 5, labels=[1, 2, 3, 4, 5])
df_rfm0效果：
# 计算RFM总分
df_rfm0['RFM_S'] = df_rfm0['R_S'].astype(int) * 5 + df_rfm0['F_S'].astype(int) * 3+ df_rfm0['M_S'].astype(int)*22. 客户细分
# 客户细分
# 最佳客户（最有价值），常购客户，⼤额消费者，不确定客户（最不值钱）
# Top，High，Medium，Low
df_rfm0['Segment'] = pd.cut(df_rfm0['RFM_S'], 4, labels=['Low', 'Medium', 'High', 'Top'])
df_rfm0
3. 气泡图
# 创建⽓泡图
print("创建⽓泡图")
# 为不同的 Segment 分配颜⾊
color_map = {'Low': 'blue', 'Medium': 'green', 'High': 'orange', 'Top': 'red'}
colors = df_rfm0['Segment'].map(color_map)
# 创建⽓泡图
plt.figure(figsize=(10, 6))
bubble_size = df_rfm0['Recency'] * 5 # 调整⽓泡⼤⼩，以便更好的可视化
plt.scatter(df_rfm0['Frequency'], df_rfm0['Monetary'], s=bubble_size, c=colors, alpha=0.5)
plt.title('Customer Segmentation Bubble Chart')
plt.xlabel('Frequency (F)')
plt.ylabel('Monetary (M)')
plt.grid(True)
# 计算 Frequency 和 Monetary 的平均值
avg_frequency0 = df_rfm0['Frequency'].mean()
avg_monetary0 = df_rfm0['Monetary'].mean()
# 添加平均值参考线
plt.axvline(x=avg_frequency, color='black', linestyle='--', linewidth=1.5, label=f'Avg Frequency: {avg_frequency0:.2f}')
plt.axhline(y=avg_monetary, color='black', linestyle='--', linewidth=1.5, label=f'Avg Monetary: {avg_monetary0:.2f}')
# 创建图例
for segment in color_map:
plt.scatter([], [], color=color_map[segment], label=segment, alpha=0.5, s=100)
plt.legend(title='Segment', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
4.分析
print("top用户分析")
top_customers0 = df_rfm0[df_rfm['Segment'] == 'Top']
# 计算平均RFM值
avg_recency0 = top_customers0['Recency'].mean()
avg_frequency0 = top_customers0['Frequency'].mean()
avg_monetary0 = top_customers0['Monetary'].mean()
# 输出结果
print(f"Top Average Recency: {avg_recency0}")
print(f"Top Average Frequency: {avg_frequency0}")
print(f"Top Average Monetary: {avg_monetary0}") 
print("“Top”客户群体不仅活跃（低Recency值）⽽且⾮常忠诚（⾼Frequency值）⾼消费能⼒")
print("top人数： ")
top_counts0 = len(top_customers0)
print(top_counts0)