RFM Scores from Customer Data

RFM (Recency, Frequency, Monetary) analysis is a technique used to identify existing customers who are most likely to respond to a new offer. This technique is commonly used in direct marketing.

Data Considerations

In a customer data file, each row represents a customer, and there is only one row (case) for each customer. If data rows represent transactions, see RFM Scores from Transaction Data.

The dataset must contain variables that contain the following information:

  • Most recent purchase date or a time interval since the most recent purchase date. This will be used to compute recency scores.
  • Total number of purchases. This will be used to compute frequency scores.
  • Summary monetary value for all purchases. This will be used to compute monetary scores. Typically, this is the sum (total) of all purchases, but it could be the mean (average), maximum (largest amount), or other summary measure.

If you want to write RFM scores to a new dataset, the active dataset must also contain a variable or combination of variables that identify each case (customer).

Creating RFM Scores from Customer Data

This feature is available in the Direct Marketing option.

  1. From the menus choose:

    Direct Marketing > Choose Technique

  2. Select Help identify my best contacts (RFM Analysis) and click Continue.
  3. Select Customer data and click Continue.
  4. Select the variable that contains the most recent transaction date or a number that represents a time interval since the most recent transaction.
  5. Select the variable that contains the total number of transactions for each customer.
  6. Select the variable that contains the summary monetary amount for each customer.
  7. If you want to write RFM scores to a new dataset, select the variable or combination of variables that uniquely identifies each customer. For example, cases could be identified by a unique ID code or a combination of last name and first name.