SQL项目实战：银行客户分析-51CTO.COM

在本文中，将与大家分享一个SQL项目，即根据从数据集收集到的信息分析银行客户流失的可能性。这些洞察来自个人信息，如年龄、性别、收入和人口统计信息、银行卡类型、产品、客户信用评分以及客户在银行的服务时间长短等。对于银行而言，了解如何留住客户比寻找其他客户更有利。

客户流失是指客户或顾客的流失。公司通常将其作为关键业务指标之一，因为恢复的长期客户对公司的价值远远高于新招募的客户。

客户流失有两种类型：自愿流失和非自愿流失。自愿流失是由于客户决定转向其他公司或服务提供商，而非自愿流失则是由于客户搬迁到长期护理机构、死亡或搬迁到较远的地方等情况造成的。

在本项目中，本文将集中讨论自愿流失，因为它可能是由于公司与客户关系中公司可以控制的因素造成的，例如如何处理账单互动或如何提供售后帮助。

【来源】:

https://en.wikipedia.org/wiki/Customer_attrition

数据集解释

【网址】：

https://www.kaggle.com/datasets/radheshyamkollipara/bank-customer-churn

图片

本文使用的是customer_churn_records表，该表包含多列，customerid是表的主键。

RowNumber：对应记录（行）编号
CustomerId：客户的ID编号
Surname：客户的姓氏
CreditScore：客户信用行为预测值
Geography：客户所在地
Gender：客户的性别信息
Age：客户的年龄信息
Tenure：客户在银行的使用年限
Balance：客户账户中的余额信息
NumOfProducts：客户购买的产品数量
HasCrCard：客户是否拥有信用卡
IsActiveMember：客户是否处于活跃状态
EstimatedSalary（估计工资）：客户的估计工资金额
Exited：客户是否离开银行
Complain：客户是否有投诉
Satisfaction Score：客户对银行的满意度评分
Card Type：客户持有的银行卡类型
Points Earned：客户使用信用卡获得的积分

# 显示表中的列 = customer_churn_records

q='''
  
  SELECT * FROM customer_churn_records

'''

df = pd.read_sql(q,engine_postgresql)
df.head()

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查询客户流失率

导入软件包

import psycopg2 # PostgreSQL数据库适配器
import pandas as pd # 用于分析数据
from sqlalchemy import create_engine # 促进Python程序与数据库之间的通信

首先，本文根据已退出的列计算有多少客户流失。

# 统计是否流失/退出的客户总数

q='''
  
  WITH temp_churn AS(
    SELECT exited,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  COUNT(exited) as Total
  FROM temp_churn
  GROUP BY 1

'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

在10000名客户中，有近20%从银行退出或流失。尽管这个数字并不算很大，但如果现在还不能解决这个问题，它可能会增长得更多。

现在，本文将从活跃客户、性别、人口统计、年龄、临时工龄、信用分数、产品数量、满意度分数、投诉、是否有信用卡、卡类型、已获积分、预估薪资和余额等多个方面来检查客户流失状况的类型。

# 统计有多少活跃客户流失

q=''' 

  WITH temp_isactivemember AS(
    SELECT exited,
    CASE 
       WHEN isactivemember = 1 THEN 'Active'
       ELSE 'Not Active'
    END AS isactivemember
    from customer_churn_records
    )
  
  SELECT isactivemember,
  COUNT (CASE WHEN exited = 1 THEN 1 END) AS Churn,
  COUNT (CASE WHEN exited = 0 THEN 1 END) AS Not_Churn
  FROM temp_isactivemember
  GROUP BY 1

'''

df = pd.read_sql(q,engine_postgresql)
df

图片

# 根据性别计算是否流失/退出的客户总数

q='''
  
  SELECT gender,
  COUNT(gender) as Total,
  COUNT(case when exited = 1 then 1 end) as Churn,
  COUNT(case when exited = 0 then 1 end) as Not_churn
  FROM customer_churn_records
  GROUP BY 1

'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据人口统计数据计算流失客户的数量

q=''' 

  SELECT geography,
  COUNT (CASE WHEN exited = 1 THEN 1 END) AS Churn,
  COUNT (CASE WHEN exited = 0 THEN 1 END) AS Not_Churn
  FROM customer_churn_records
  GROUP BY 1

'''

df = pd.read_sql(q,engine_postgresql)
df

图片

#根据年龄组计算流失客户的数量

q=''' 

    SELECT 
    CASE 
        WHEN age <= 20 THEN 'Group <= 20'
        WHEN age >= 21 AND age <= 40 THEN 'Group 21-40'
        WHEN age >= 41 AND age <= 60 THEN 'Group 41-60'
        ELSE 'Group > 60'
    END AS age_category,
    COUNT(CASE WHEN exited = 1 then 1 end) as Churn,
    COUNT(CASE WHEN exited = 0 then 1 end) as Not_Churn
    FROM customer_churn_records
    GROUP BY 1
    ORDER BY 1

'''

df = pd.read_sql(q,engine_postgresql)
df

图片

# 根据他们成为客户的时间计算是否流失/退出的客户总数

q='''
  
  WITH temp_tenure AS(
    SELECT tenure,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(tenure) as Average_tenure
  FROM temp_tenure
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据信用评分计算有多少客户流失
#（平均值、最高值和最低值）

q='''

   WITH temp_creditscore AS(
    SELECT creditscore,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(creditscore) as avg_creditscore,
  MAX(creditscore) as max_creditscore,
  MIN(creditscore) as min_creditscore
  FROM temp_creditscore
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户使用银行产品的数量，计算有多少客户流失

q='''

   WITH temp_bankprod AS(
    SELECT numofproducts,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(numofproducts) as avg_numofproducts
  FROM temp_bankprod
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户 对银行的满意度得分的平均得分，计算有多少客户流失

q='''

   WITH temp_satisfaction AS(
    SELECT satisfaction_score,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(satisfaction_score) as satisfaction_level
  FROM temp_satisfaction
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户的投诉量，统计有多少客户流失

q='''

   WITH temp_complain AS(
    SELECT complain,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  COUNT(complain) as complain
  FROM temp_complain
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户是否拥有信用卡计算有多少客户流失

q=''' 

    WITH temp_hascrcard AS(
    SELECT hascrcard,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  COUNT (CASE WHEN hascrcard = 1 THEN 1 END) AS has_creditcard,
  COUNT (CASE WHEN hascrcard = 0 THEN 1 END) AS no_creditcard
  FROM temp_hascrcard
  GROUP BY 1

'''

df = pd.read_sql(q,engine_postgresql)
df

图片

# 根据卡的类型计算流失客户的数量

q='''

   WITH temp_card AS(
    SELECT card_type,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  COUNT(CASE WHEN card_type = 'PLATINUM' THEN 1 END) as PLATINUM,
  COUNT(CASE WHEN card_type = 'DIAMOND' THEN 1 END) as DIAMOND,
  COUNT(CASE WHEN card_type = 'GOLD' THEN 1 END) as GOLD,
  COUNT(CASE WHEN card_type = 'SILVER' THEN 1 END) as SILVER
  FROM temp_card
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户获得的积分计算有多少客户流失

q='''

   WITH temp_point AS(
    SELECT point_earned,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(point_earned) as avg_point,
  MAX(point_earned) as max_point,
  MIN(point_earned) as min_point
  FROM temp_point
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户的预估工资计算有多少客户流失

q='''

   WITH temp_salary AS(
    SELECT estimatedsalary,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(estimatedsalary) as avg_salary,
  MAX(estimatedsalary) as max_salary,
  MIN(estimatedsalary) as min_salary
  FROM temp_salary
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

# 根据客户的银行存款余额计算有多少客户流失
#（平均、最高、最低）

q='''

   WITH temp_balance AS(
    SELECT balance,
    CASE 
       WHEN exited = 1 THEN 'Churn'
       ELSE 'Not Churn'
    END AS STATUS
    from customer_churn_records
    )
  
  SELECT STATUS,
  AVG(balance) as avg_balance,
  MAX(balance) as max_balance,
  MIN(balance) as min_balance
  FROM temp_balance
  GROUP BY 1
  
'''

df = pd.read_sql(q,engine_postgresql)
df.head()

图片

结论

根据上述问题，有一些类别可以帮助确定哪些方面会真正影响客户流失。不管客户在银行停留了多长时间，他们仍然有可能流失，或者说，客户的银行账户上有相当数量的存款，他们仍然有可能流失。

41至60岁年龄段的客户比其他年龄段的客户更容易流失。为了解决这个问题，银行可以集中精力创造或提升产品和服务，以帮助吸引和维护特定年龄段的客户，比如为年龄较大的客户提供更流畅的服务和最短的排队时间。

持有信用卡的客户往往不会流失，而是会继续留在银行。银行最好通过各种促销活动说服更多的客户申请信用卡，这取决于客户细分，可根据客户的卡种（钻石卡、白金卡、金卡、银卡）、性别、年龄、支出和人口分布进行细分。

留存客户和流失客户的满意度得分有点令人担忧 [ 3.017960 / 2.997547 ]。银行需要进行评估，以保持流失客户和留存客户之间的满意度得分差距，并保持活跃客户，因为活跃客户流失的可能性较低。