RFM Analysis

This topic demonstrates how to set up a dashboard that allows you to segment your customers by their recency, frequency, and monetary rankings. RFM analysis is a marketing technique that takes customer behaviors into account to help you determine segmentation for outreach. It accounts for three aspects:

1. Recency in how recently a customer purchased from your store
2. Frequency in how often they purchase from you
3. Monetary in how much the customer spends

The RFM analysis can only be configured if you have the Adobe Commerce Intelligence Pro plan on the new architecture (for example, if you have the Data Warehouse Views option under the Manage Data menu). These columns can be created from the Manage Data > Data Warehouse page. Detailed instructions are provided below.

Getting Started

You need to first upload a file containing just a primary key with the value of one. This allows the creation of some necessary calculated columns for the analysis.

You can use this article and the image below to format your file.

Calculated Columns

A further distinction is made if your business allows guest orders. If so, you can ignore all steps for the customer_entity table. If guest orders are not allowed, ignore all steps for the sales_flat_order table.

Columns to create

• Sales_flat_order/customer_entity table

• Customer's last order date

• Column type: Many to one > Max

• Pat: sales_flat_order.customer_id > customer_entity.entity_id

• Selected column: created_at

• Filter: Orders we count

• Seconds since customer's last order date
  

• Column type: – "Same table > Age

• Selected column: Customer's last order date

• (input) Count reference

• Column type: Same table > Calculation

• Inputs: entity_id

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

• Datatype: Integer

• Count reference table (this is the file you uploaded with the number “1”)

• Number of customers

• Column type: Many to One > Count Distinct

• Path: ales_flat_order.(input) reference > Count reference.Primary Key OR customer_entity.(input)reference > Count Reference. Primary Key

• Selected column: sales_flat_order.customer_email OR customer_entity.entity_id

• Customer_entity table

• Number of customers

• Column type: One to Many > JOINED_COLUMN

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

• Selected column: Number of customers

• (input) Ranking by customer lifetime revenue

• Column type: Same table > Event Number

• Event owner: (input) reference for count

• Event rank: Customer's lifetime revenue

• Ranking by customer lifetime revenue

• Column type: Same table > Calculation

• Inputs: (input) Ranking by customer lifetime revenue, Number of customers

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

• Datatype: Integer

• Customer’s monetary score (by percentiles)

• Column type: Same table > Calculation

• Inputs: (input) Ranking by customer lifetime revenue, Number 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

• Datatype: Integer

• (input) Ranking by customer lifetime number of orders

• Column type: Same table > Event Number

• Event owner: (input) reference for count

• Event rank: Customer's lifetime number of orders

• Ranking by customer lifetime number of orders

• Column type: – “Same table > Calculation”

• Inputs: – (input) Ranking by customer lifetime number of orders, Number of customers

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

• Datatype: – Integer

• Customer’s frequency score (by percentiles)

• Column type: Same table > Calculation

• Inputs: (input) Ranking by customer lifetime number of orders, Number 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

• Datatype: Integer

• Ranking by seconds since customer’s last order date

• Column type: Same table > Event Number

• Event owner: (input) reference for count

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

• Customer’s recency score (by percentiles)

• Column type: Same table > Calculation

• Inputs: (input) Ranking by customer lifetime number of orders, Number 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

• Datatype: Integer

• Customer’s recency score (by percentiles)

• 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

• Datatype: String

• Count reference table

• Number of customers: (RFM > 0)

• Column type: Many to One > Count Distinct

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

• Selected column: sales_flat_order.customer_email OR customer_entity.entity_id

• Filter: Customer's RFM score (by percentile) Not Equal To 000

• Customer_entity table

• Number of customers: (RFM > 0)

• Column type: One to Many > JOINED_COLUMN

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

• Selected column: – Number of customers

• Customer’s recency score (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

• Datatype: Integer

• (input) Ranking by customer’s overall RFM score

• 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) Not Equal To 000

• Ranking by customer’s overall RFM score

• Column type: Same table > Calculation

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

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

• Datatype: Integer

• Customer’s RFM group

• Column type: Same table > Calculation

• Inputs: (input) Ranking by customer lifetime revenue, Number 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

• Datatype: Integer

Metrics

No new metrics!

Reports

• Customers by RFM grouping

• Metric A: New customers

• Metric: New customers

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

• Time period: All time

• Interval: None

• Hide chart

• Group by: Customer's RFM group

• Group by: Email

• Chart type: Table

• Customers with five recency score

• Metric A: New customers

• Metric: New customers

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

• Time period: All time

• Interval: None

• Chart Type: Scalar

• Hide chart

• Group by: Email

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

• Chart type: Table

• Customers with one recency score

• Metric A: New customers

• Metric: New customers

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

• Time period: All time

• Interval: None

• Chart Type: Scalar

• Hide chart

• Group by: Email

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

• Chart type: Table

After compiling all the reports, you can organize them on the dashboard as you desire. The result may look like the above sample dashboard, but the three generated tables are just examples of the types of customer segmentation you can perform.