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RFM分析

本主题将演示如何设置一个功能板,以允许您按回访间隔、频度和货币排名对客户进行分段。 RFM分析是一种营销技术,它考虑客户行为,帮助您确定外联的分段。 它包含三个方面:

  1. 客户最近从您的商店购买的回访间隔
  2. 他们向您购买的频率
  3. 客户花费的金额

显示回访间隔、频度和货币值区段的RFM分析仪表板

只有在新架构上具有Adobe Commerce Intelligence Pro计划时(例如,如果在Data Warehouse Views菜单下具有Manage Data选项),才能配置RFM分析。 可从​ Manage Data > Data Warehouse ​页面创建这些列。 下面提供了详细说明。

快速入门

您需要首先上传一个文件,其中只包含值为1的主键。 这允许为分析创建一些必要的计算列。

您可以使用此文章和下面的图像来格式化您的文件。

计算列

如果您的企业允许客户订购,则需作进一步的区分。 如果是,则可以忽略customer_entity表的所有步骤。 如果不允许来宾订单,请忽略sales_flat_order表的所有步骤。

要创建的列

  • Sales_flat_order/customer_entity ​表

  • Customer's last order date

  • Column type: Many to one > Max

  • Pat: sales_flat_order.customer_id > customer_entity.entity_id

  • 已选择column: created_at

  • Filter: Orders we count


  • 自客户上次订购日期起 秒

    • Column type: - “同一张表>年龄
  • 已选择column: Customer's last order date

  • (输入)计数引用

  • Column type: Same table > Calculation


  • 输入: entity_id

  • Calculation: **case when A is null then null else 1 end**


  • 数据类型: Integer

  • 计数引用 ​表(这是您上传的编号为“1”的文件)

  • 客户数量

  • Column type: Many to One > Count Distinct

  • Path: ales_flat_order.(input) reference > Count reference.Primary Keycustomer_entity.(input)reference > Count ReferencePrimary Key

  • 已选择column: sales_flat_order.customer_emailcustomer_entity.entity_id

  • Customer_entity ​表

  • 客户数量

  • Column type: One to Many > JOINED_COLUMN

  • Path: customer_entity。(输入)参考>客户集中。Primary Key

  • 已选择column: Number of customers

  • (输入) Ranking by customer lifetime revenue

  • Column type: Same table > Event Number

  • Event owner: (input) reference for count

  • Event rank: Customer's lifetime revenue

  • 按客户存留期收入排名

  • Column type: Same table > Calculation

  • Inputs: (input) Ranking by customer lifetime revenueNumber of customers

  • Calculation: case when A is null then null else (B-(A-1)) end


  • 数据类型: Integer

  • 客户的货币得分(以百分位数为单位)

  • Column type: Same table > Calculation

  • Inputs: (input) Ranking by customer lifetime revenueNumber of customers

  • Calculation: Case when round((B-A+1)*100/B,0) <= 20 then 5 when round((B-A+1)*100/B,0) <= 40 then 4 when round((B-A+1)*100/B,0) <= 60 then 3 when round((B-A+1)*100/B,0) <= 80 then 2 when round((B-A+1)*100/B,0) <= 100 then 1 else 0 end


  • 数据类型: Integer

  • (输入)按客户存留期订单数排名

  • Column type: Same table > Event Number

  • Event owner: (input) reference for count

  • Event rank: Customer's lifetime number of orders

  • 按客户存留期订单数排名


  • 列类型: – “相同表>计算”

  • Inputs: - (输入)按客户存留期订单数 ​排名,客户数

  • Calculation: - 当A为null时为null,否则(B-(A-1))结束

  • Datatype: — 整数

  • 客户的频率得分(按百分位数)

  • Column type: Same table > Calculation

  • Inputs: (input) Ranking by customer lifetime number of ordersNumber of customers

  • Calculation: Case when round((B-A+1)*100/B,0) <= 20 then 5 when round((B-A+1)*100/B,0) <= 40 then 4 when round((B-A+1)*100/B,0) <= 60 then 3 when round((B-A+1)*100/B,0) <= 80 then 2 when round((B-A+1)*100/B,0) <= 100 then 1 else 0 end


  • 数据类型: Integer

  • 自客户上次订购日期以来按秒排名

  • Column type: Same table > Event Number

  • Event owner: (input) reference for count

  • Event rank: Seconds since customer's last order date

  • 客户的回访间隔分数(按百分位数)

  • Column type: Same table > Calculation

  • Inputs: (input) Ranking by customer lifetime number of ordersNumber of customers

  • Calculation: Case when (A * 100/B,0) <= 20 then 5 when (A * 100/B,0) <= 40 then 4 when (A * 100/B,0) <= 60 then 3 when (A * 100/B,0) <= 80 then 2 when (A * 100/B,0) <= 100 then 1 else 0 end


  • 数据类型: Integer

  • 客户的回访间隔分数(按百分位数)

  • Column type: Same table > Calculation

  • Inputs: Customer's recency score (by percentiles)Customer's frequency score (by percentiles)Customer's monetary score (by percentiles)

  • Calculation: case when (A IS NULL or B IS NULL or C IS NULL) then null else concat(A,B,C) end


  • 数据类型: String

  • 引用计数 ​表

  • Number of customers: (RFM > 0)

  • Column type: Many to One > Count Distinct

  • Path: sales_flat_order.(input) reference > Customer Concentration. Primary Keycustomer_entity.(input)reference > Customer Concentration.Primary Key

  • 已选择column: sales_flat_order.customer_emailcustomer_entity.entity_id

  • Filter: Customer's RFM score (by percentile)不等于000

  • Customer_entity ​表

  • Number of customers: (RFM > 0)

  • Column type: One to Many > JOINED_COLUMN

  • Path: customer_entity.(input) reference > Customer Concentration.Primary Key

  • 已选择column: - Number of customers

  • 客户的回访间隔分数(R+F+M)

  • Column type: Same table > Calculation

  • Inputs: - Customer's recency score (by percentiles)Customer's frequency score (by percentiles)Customer's monetary score (by percentiles)

  • Calculation: case when (A IS NULL or B IS NULL or C IS NULL) then null else A+B+C end


  • 数据类型: Integer

  • (输入)按客户的整体RFM得分排名

  • Column type: Same table > Event Number

  • Event owner: (input) reference for count

  • Event rank: Customer's recency score (R+F+M)

  • Filter: Customer's RFM score (by percentile)不等于000

  • 按客户的整体RFM分数排名

  • Column type: Same table > Calculation

  • Inputs: (input) Ranking by customer's overall RFM scoreNumber of customers (RFM > 0)

  • Calculation: case when A is null then null else (B-(A-1)) end


  • 数据类型: Integer

  • 客户的RFM组

  • Column type: Same table > Calculation

  • Inputs: (input) Ranking by customer lifetime revenueNumber of customers

  • Calculation: Case when round(A * 100/B,0) <= 20 then '5. copper' when round(A * 100/B,0) <= 40 then '4. bronze' when round(A * 100/B,0) <= 60 then '3. silver' when round(A * 100/B,0)<= 80 then '2. gold' else '1. Platinum' end


  • 数据类型: Integer

NOTE
使用的百分比甚至是对客户的分段(例如,20%存储段可返回1-5)。 如果您希望通过自定义方式对这些内容进行加权,请在提交票证时告知分析人员。

量度

无新量度!

NOTE
确保在生成新报告之前将所有新列作为维度添加到量度

报告

  • 按RFM分组​ 客户

  • 量度ANew customers

  • Metric: New customers

  • Filter: Customer's RFM score (by percentiles) Not Equal to 000

  • Time period: All time


  • Interval: None

  • 隐藏图表

  • Group by: Customer's RFM group


  • 分组依据: Email


  • Chart type: Table

  • 有五个回访间隔分数的客户

  • 量度ANew customers

  • Metric: New customers

  • Filter: Customer's recency score (by percentiles) Equal to 5

  • Time period: All time


  • Interval: None


  • Chart Type: Scalar

  • 隐藏图表


  • 分组依据: Email

  • Group by: Customer's RFM score (R+F+M)


  • Chart type: Table

  • 具有一个回访间隔分数的客户

  • 量度ANew customers

  • Metric: New customers

  • Filter: Customer's recency score (by percentiles) Equal to 1

  • Time period: All time


  • Interval: None


  • Chart Type: Scalar

  • 隐藏图表


  • 分组依据: Email

  • Group by: Customer's RFM score (R+F+M)


  • Chart type: Table

在编译所有报告后,您可以根据需要将报告组织在功能板上。 结果可能类似于上面的示例仪表板,但生成的三个表只是您可以执行的客户分段类型的示例。

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