RFM Analysis (Recency, Frequency, Monetary)

The dataset under study contains credit card transactions from the State of Oklahoma. It includes information on purchases made through the purchase card programs administered by the state and higher educational institutions. The purchase card information is updated monthly after the end of every month. As such, July information is added in August, August in September, and so on and so forth.

In a brief, here's the data description:

• Dataset contains around 440k credit card transactions.
• Each row in the dataset refers to a credit card transaction by a cardholder.
• Each cardholder belongs to an agency.
• Each transaction has the following information:
  • cardholder name
  • agency
  • amount
  • transaction date
  • merchant name (vendor)
  • merchant category
  • description of the expense

Conclusion & Recommendation

Who are our Top Customers?

Insights

1. Using RFM analysis we can identify whos are best customer and target them with more direct marketing stratgy based on their purchasing behavior.
2. We can identify lost cheap customer and stratigize on how we can inrease their RFM score.

Recommendation

1. Product cross selling: we can identify which products are commonly purchased together, allowing them to create targeted product bundles or recommend complementary products to customers.
2. Churn Rate: By monitoring changes in RFM scores over time, businesses can identify which customers are at risk of churning and take proactive steps to prevent it.
3. Customer segmentation: we can group each customer base on theor RFM scores and identify each customer segment and tailor our marketing strategy accordingly.