Campaign Optimization using RFM Analysis 
You can use this process in database marketing to predict response rates and optimize your campaigns. It can help you use historical data about customer behavior and similar campaigns to identify the optimum target group for your current campaign. Recency, frequency, monetary value (RFM) analysis improves the profitability of campaigns and optimizes the Return on Investment (ROI).
A RFM analysis uses the following parameters to predict the probability of a response to a campaign. These three values are applied to divide customers into segments.
How long ago a customer made a purchase (Recency (R))
R is the factor having most influence on the predicted response probability
How often a customer makes a purchase (Frequency (F))
F is less influential
How much a customer spends on a purchase (Monetary Value (M))
M value is assumed to have least influence on response rates
Along with sales orders, you can also draw on other behavioral criteria, such as calls received or inquiries made by email. The system also allows several approaches to be used in parallel as RFM segmentation models.
In segmentation, the system assigns each customer to a specific RFM Segment.
Note
To establish the appropriateness of this procedure to your field
of business, use the options proposed by SAP. For information about these
options, see the SAP Library for 
SAP NetWeaver
Release "04 Information
Integration SAP Business Information Warehouse
BI Content Customer Relationship
Management CRM Analytics
Marketing Analyses Response
Prediction Modeling Campaign Optimization
using RFM Analysis Checking the RFM Analysis
.
Prerequisites
RFM analysis can only be applied to customers for whom you have behavioral data. For the definition of target groups, however, it is common practice to also use customer addresses taken from external providers, even though there is no behavioral data for these customers. Only those customers in the target group with such data are considered during valuation and optimization.
If you would like to use the RFM analysis functions provided by SAP, you need the following software components in mySAP.com:
SAP Customer Relationship Management (SAP CRM) with Release 3.0 and higher.
In SAP CRM, RFM analysis is integrated into segmentation and allows you to predict the response rate and to simulate profitability and ROI with the different target groups you have determined.
SAP Business Information Warehouse (SAP NetWeaver BI) with Release 2.1C (Support Package 8) and higher.
In SAP NetWeaver BI, the options for determining the RFM segmentation and the RFM response rates are combined in a single transaction for RFM modeling.
Process
The following steps provide a general overview of the process flow for an RFM analysis:
Create a representative campaign.
Determine the segmentation of the customer base for the time when the campaign is to be launched.
For more detailed information, see the SAP Library for
SAP NetWeaver
Sap NetWeaver 2004 Information
Integration SAP Business Information Warehouse
BI Content Customer Relationship
Management CRM Analytics
Marketing Analyses Response
Prediction Modeling Campaign Optimization
using RFM Analysis Segmentation
.When the campaign has been executed, calculate the response rate for each RFM segment.
For more information, see the SAP Library for
SAP NetWeaver
Sap NetWeaver 2004 Information
Integration SAP Business Information Warehouse
BI Content Customer Relationship
Management CRM Analytics
Marketing Analyses Response
Prediction Modeling Campaign Optimization
using RFM Analysis Response Rate Calculation
.Determine the updated segmentation of the customer base (see the section Segmentation).
This provides you with the information about which customer currently belongs to which RFM segment and what the response rate is for that RFM segment.
Transfer the probability of a response from a customer to SAP CRM (see the section Response Rate Calculation).
Use simulation to optimize the target group for a campaign.