• A product (Product ID)
  
• A related transaction (Transaction ID)
  
• Number of products purchased in a transaction (Product Quantity)
  
• The product purchase price (Product Price)
  
• Transaction date (Date)
  
• Customer who made the purchase (Customer ID, Customer Name)
  

1. Gather and Prepare Transaction Data : 

2. Compute RFM Metrics: 

Recency (R)

Monetary Value (M)

RFM Query Table

3. Segment Customers using Cluster Analysis

1. Click the Create New icon and choose New chart from the drop-down menu.
   
2. Add the columns to the chart shelf as shown below,
   
1. X-axis: Customer Name
   
2. Y-axis: Monetary Value with Sum function.
   
3. Click Generate Graph and change the chart type to bar chart.
   
   
4. Click the Analysis icon and select Cluster Analysis > Add Clusters.
   
5. The Model is chosen automatically based on the columns dropped in the shelves.
   
6. By default, the columns dropped in the shelves (Monetary Value) are selected as factors. Click the drop-down icon to include Recency and Frequency columns as factors.
   
   
7. The number of clusters is determined automatically but can be adjusted based on business needs and specific customer segmentation goals to ensure optimal categorization.
   
8. Choose the Normalization method to prevent values of high ranges from dominating the results. For instance, Recency (measured in days) and Monetary Value (measured in currency) have different scales, and normalization ensures a balanced contribution from each metric.
9. Click Apply.
   
   

4. Export Clusters Data

Build an RFM Analysis Dashboard

1. Import the Clusters Data

2. Create Reports to Understand the Characteristics of the Clusters

Clusters vs Monetary Value