Segmenting Customers Using RFM in Tableau

To unlock valuable customer insights from the Kaggle E-Commerce dataset, I embarked on an RFM (Recency, Frequency, Monetary) analysis. My goal was to segment customers based on their relationship status, age group, and overall purchasing behavior. This involved creating key calculated fields:

• Recency Score: Defining customer engagement levels based on the time since their last purchase (e.g., Very Active, Active, At-Risk, Lost).

• Purchase Frequency: Quantifying how often customers make purchases.

• Monetary Value: Determining the total amount customers have spent.

By combining these RFM components, I was able to assign each customer an RFM segment, revealing valuable groups such as Loyal customers, those at Risk of being Lost, and our top Champions.

Total Spent by each Recency Group

Total Spent vs Total Purchase Count - With the filter, we are able to see which of the group has a correlation between both variables

Top 30 Highest Spending Customers

Customer Count by RFM Category

These RFM insights reveal a critical retention opportunity—high-value 'At Risk' customers drive 16% of revenue, while a staggering 1,697 'Lost' customers highlight leakage in your funnel. By targeting lapsing spenders and refining segment thresholds, you can recover revenue and stabilize your customer base.