An Introduction to RFM in Analytics

Copy r ight © 2012, SAS Ins t i tu te Inc . A l l r ights reserved.

RFM QUICK START GUIDEPAT VALENTE/ROB WILSONPRE-SALES, SAS CANADA


RFM MODEL QUICK START CONTENTS

• This guide will provide you with the following:

• Introduction to the RFM model• Data Requirements• SAS project configuration considerations• Model Description• Workflow Overview and Build


INTRODUCTION


RFM MODEL INTRODUCTION

• WHAT IS RFM?• Method used for analyzing customer value. • Commonly used in database marketing and direct marketing.• Recency - How recently did the customer purchase?• Frequency - How often do they purchase?• Monetary Value - How much do they spend?

This quick start guide provides an Enterprise Guide project that categorizes

customers into a predefined number of ‘segments’ based on the score from

the RFM analysis.


DATA REQUIREMENTS


RFM MODEL DATA REQUIREMENTS

• The input data for this project is transactional data. The project aggregates the

transaction data into customer data. In transactional data, each record represents

one transaction.• Minimum data requirements:

• There are three variables required as listed below:• Transaction Date: the variable that specifies the date of the transaction. This information

determines the most recent transaction date. In this step, the data is sorted in descending

transaction date order and the latest transaction date is selected in a query.• Amount of Transaction: the variable that specifies the amount of the transaction. This data

is used to compute the total amount of the transactions. • Customer Identifier: the variable that contains a number or string that can uniquely identify

a customer.


PROJECT CONFIGURATION


RFM MODEL PROJECT CONFIGURATION

• When requesting the project please specify whether you are running Enterprise Guide

locally on a desktop or via a server with a Metadata layer.• If you have the incorrect version, you can either request the correct version or simply

using the Migration Wizard in SAS to convert the existing project.• Find the MigrationWizard executable file in the EG folder of SASHome and double-click

to run. Ensure that all other programs are closed or the wizard may not work properly.



• Click “Next” after you see the first

step.

• In step 2, click “Modify” to change the

active connection you have to SAS.



• In step 3, find the project you need to

convert, check it and send it to the

Selected files box.

• Click “Next” in step 4.



• Click “Done” when

parsing is completed.

• In step 5 click in the New Mapping drop down column to

select the correct local references. Click “Next”.



• Click “Done” when the migration process is

complete. The project will be ready to be

opened in Enterprise Guide.

• Click “Finish” in step 6 to

complete the migration.


MODEL DESCRIPTION


RFM MODEL MODEL DESCRIPTION

• Recency, frequency, and monetary scores are determined as follows: • The recency score is determined by sorting the values of the most recent

transaction date in ascending order and then grouping these values into ‘bins’. The

bin with the oldest dates is assigned the lowest recency score, and so on. The

number of bins and recency score for each bin will need to be determined by the

customer.• The frequency score is determined by sorting the values of the number of

transactions in ascending order and also grouping these values into ‘bins’. The bin

with the smallest number of transactions is assigned the lowest frequency score,

and so on. The number of bins and frequency score for each bin will need to be

determined by the customer.


RFM MODEL MODEL DESCRIPTION

• The monetary score is determined by sorting the values of the total amount of the

transactions in ascending order and grouping these values into ‘bins’. The bin with

the smallest amount is assigned the lowest monetary score, and so on. The

number of bins and monetary score for each bin will need to be determined by the

customer.

Categorizing the data into bins• You will need to specify the number of bins for recency, frequency, and monetary

values individually. • RFM score = Recency score + Frequency score + Monetary score. • The least favorable customer segment has the lowest RFM score. The most

favorable customer segment has the highest RFM score.


PROJECT WORKFLOW OVERVIEW AND BUILD


RFM MODEL WORKFLOW OVERVIEW

• Below is a screen shot of the Enterprise Guide project for calculating the RFM

score and creating the segments based on the RFM score. You will see the

initial data set on the far left of the workflow.



1. The first step is to calculate

the most recent transaction

date, using the “MAX”

statistic selection in the query

builder. Right click on the

Recent transactions icon and

select “Modify” and

“Computed Columns”.

2. You can see the new column

named “Max_Trans_Date”

3. You can see the details of the

calculation which is a Max of

the transaction date.



1. Next you will calculate the

total transactions using the

‘COUNT’ statistic and the

customer IDs. Right click on

the Total transactions icon

and select “Modify” and

“Computed Columns”.


named “Total_Transactions”.


calculation which is a

frequency count for each of

the unique customer IDs.



1. The third step is to calculate

the total transaction amount

using the ‘SUM’ statistic and

the transaction amounts.

Right click on the Total

transactions icon and select

“Modify” and “Computed

Columns”.


named “SUM

_of_Transaction_Amount”.


calculation which is a sum of

the transaction amounts.



1. The next step is to add

the newly calculated

columns to the data by

joining the tables.

2. Select Tasks Data

Query Builder.

3. You will see the new

columns in the Select

Data tab.

4. Click on Join Tables to

see how the tables were

joined using Customer ID

as the common key.



1. You can now create scores for

recency, frequency and

monetary bins individually. Right-

click on the Create RFM Scores

icon and select “Modify”.

2. You will see the new columns in

the Select Data tab.

3. Click on “Computed Columns”,

select one of the scores and

click “Edit”.



1. You can then see how each

score has been created using

a “case” statement and

creating “bins” based on the

values of the variable.

2. This example shows a sample

calculation for frequency with

3 ‘bins’ and a score for each

bin (1, 2 or 3).

3. You can click “Next” to

complete the build.

4. You can repeat these steps

for the other scores.



1. The RFM scores are now

added together to create

the Total RFM score. Right-

click on the Total RFM

Score icon and select

“Modify”.

2. You will see the

Total_RFM_Score columns

in the Select Data tab.

3. Click on “Computed

Columns”, select the Total

RFM Score and click “Edit”.



1. You can then see how the

Total RFM Score has been

created simply by adding the

three calculated values

together.


complete the build.

3. The Total RFM Score is then

added to the data for each

Customer ID.



1. Once the number of segments

and the split values of the

segments has been determined,

the segment variable is created.

2. Right-click on the “Segment”

icon and select “Modify”.

3. You will see the “Segment”

column in the Select Data tab.

4. Click on “Computed Columns”,

select the “Segment” variable

and click “Edit”.



1. You can see how a “case”

statement is used to create

each segment based on the

values of the Total RFM Score

and how many segments are

desired.


complete the build.

3. The Segment variable is then

added to the data set.



1. In the final step, a pie chart illustrating the frequency counts of the customers in each segment is created.

2. Right-click on the Pie Chart icon and select “Modify” to examine the attributes that can be customized in creating the chart.

Note:• This method is descriptive only, and does not

provide a mechanism to forecast behavior as a predictive model might.

• When used to target customers, it assumes that customers are likely to continue behaving in the same manner. That is, it does not take into account the impact of life stage or life cycle transitions on likelihood of response.

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Data & Analytics

An Introduction to RFM in Analytics