Drilling Down Home Page Turning Customer Data
into Profits with a Spreadsheet
The Guide to Maximizing Customer Marketing ROI

Site Map

Book Includes all tutorials and examples from this web site

Get the book!

Purchase Drilling Down Book

Customers Speak Up on Book & Site

About the Author

Workshops, Project Work: Retail Metrics & Reporting, High ROI
Customer Marketing

Marketing Productivity Blog

8 Customer
Promotion Tips


Customer Retention

Customer Loyalty

High ROI Customer Marketing: 3 Key Success Components

LifeTime Value and
True ROI of Ad Spend

Customer Profiling

Intro to Customer
Behavior Modeling

Customer Model:

Customer Model:

Customer Model:
Recent Repeaters

Customer LifeCycles

LifeTime Value

Calculating ROI

Mapping Visitor

Measuring Retention
in Online Retailing

Measuring CRM ROI

CRM Analytics:
Micro vs. Macro

Pre-CRM Testing for
Marketing ROI

Behavior Profiling

See Customer
Behavior Maps

Favorite Drilling
Down Web Sites

About the Author

Book Contents

 What is in the book?
 Productivity Blog
  Simple CRM
 Customer Retention
 Relationship Marketing
 Customer Loyalty
 Retail Optimization
  Visitor Conversion
  Visitor Quality
Guide to E-Metrics
  Customer Profiles
  Customer LifeCycles
  LifeTime Value
  Calculating ROI

  Recent Repeaters
  Retail Promotion
  Pre-CRM ROI Test
  Tracking CRM ROI
Tutorial: Latency
  Tutorial: Recency
  Scoring Software
  About Jim

Customer Response, Retention and Valuation Concepts (RFM Model)

Jim's Intro:  Here's a more complex model using Recency and Frequency to rank the LifeTime Value and likelihood to respond of customers relative to each other.

Have you ever heard somebody refer to his or her customer list as a "file"? If you have, you were probably listening to someone who has been around the catalog block a few times.   Before computers (huh?), catalog companies used to keep all their customer information on 
3 x 5 cards.

Theyíd rifle through this deck of cards to select customers for each mailing, and when a customer placed an order, they would write it on the customerís card.  These file cards as a group became known as "the customer file," and even after everything became computerized, the name stuck.

Who cares? It happens that while going through these cards by hand, and writing down orders, the catalog folks began to see patterns emerge.  There was an exchange taking place, and the data was speaking.  What the data said to them, what they heard, were 3 things:

1.  Customers who purchased recently were more likely to buy again versus customers who had not purchased in a while

2.  Customers who purchased frequently were more likely to buy again versus customers who had made just one or two purchases

3.  Customers who had spent the most money in total were more likely to buy again.  The most valuable customers tended to continue to become even more valuable.

So the catalog folks tested this concept, the idea past purchase behavior could predict future results.  First, they ranked all their customers on these 3 attributes, sorting their customer records so that customers who had bought most Recently, most Frequently, and had spent the most Money were at the top.  These customers were labeled "best."   Customers who had not purchased for a while, had made few purchases, and had spent little money were at the bottom of the list, and these were labeled "worst."

Then they mailed their catalogs to all the customers, just like they usually do, and tracked how the group of people who ranked highest in the 3 categories above (best) responded to their mailings, and compared this response to the group of people who ranked lowest (worst).  They found a huge difference in response and sales between best and worst customers.  Repeating this test over and over, they found it worked every time!

The group who ranked "best" in the 3 categories above always had higher response rates than the group who ranked "worst."  It worked so well they cut back on mailing to people who ranked worst, and spent the money saved on mailing more often to the group who ranked best.  And their sales exploded, while their costs remained the same or went down.  They were increasing their marketing efficiency and effectiveness by targeting to the most responsive, highest future value customers.

The Recency, Frequency, Monetary value (RFM) model works everywhere, in virtually every high activity business.  And it works for just about any kind of "action-oriented" behavior you are trying to get a customer to repeat, whether itís purchases, visits, sign-ups, surveys, games or anything else.  Iím going to use purchases and visits as examples.

A customer who has visited your site Recently (R) and Frequently (F) and created a lot of Monetary Value (M) through purchases is much more likely to visit and buy again.  And, a high Recency / Frequency / Monetary Value (RFM) customer who stops visiting is a customer who is finding alternatives to your site.  It makes sense, doesnít it? 

Customers who have not visited or purchased in a while are less interested in you than customers who have done one of these things recently.  Put Recency, Frequency, and Monetary Value together and you have a pretty good indicator of interest in your site at the customer level.  This is valuable information to for a business to have.

How is RFM implemented?  Customers are ranked based on their R, F, and M characteristics, and assigned a "score" representing this rank.   Assuming the behavior being ranked (purchase, visit) using RFM has economic value, the higher the RFM score, the more profitable the customer is to the business now and in the future.  High RFM customers are most likely to continue to purchase and visit, AND they are most likely to respond to marketing promotions.  The opposite is true for low RFM score customers; they are the least likely to purchase or visit again AND the least likely to respond to promotions.

For these reasons, RFM is closely related to another customer marketing concept: LifeTime
(LTV).  LTV is the expected net
profit a customer will contribute to your business over the LifeCycle, the period of time a customer remains a customer.  Because of the linkage to LTV and the LifeCycle, RFM techniques can be used as a proxy for the future profitability of a business.

High RFM customers represent future business potential, because the customers are willing and interested in doing business with you, and have high LTV.  Low RFM customers represent dwindling business opportunity, low LTV, and are a flag something needs to be done with those customers to increase their value.

Once you have scored customers using RFM, you will be able to:

  • Decide who to promote to and predict the response rate
  • Optimize promotional discounting by maximizing response rate while reducing overall discount costs
  • Determine which parts of the site or activities attract high value customers and focus on them to increase customer loyalty and profitability

The Drilling Down book teaches RFM customer scoring methods and how to use the scores to create high ROI marketing and site designs.  The software that comes with the book automates the customer scoring process, importing your customer transaction files, creating a customer database, and assigning a score to each customer based on RFM theory.

You will learn the original RFM scoring method and theory, plus the updated and modified version based on my experience using RFM with interactive environments.  This version simplifies customer analysis by emphasizing the visual display of customer behavior to aid in marketing decision making.  You don't need a Ph.D. to use the Drilling Down method.

For example, the standard approach to RFM analysis is a "snapshot" method, measuring the customer at a point in time.  The methods described in the Drilling Down book modify this approach.  These new methods make use of the RFM parameters over time in unique ways, and are not dependent on purchase dollars (monetary) as the original RFM model is.  The result of using these methods is very high ROI marketing campaigns & site designs.

Want a sample of this method at work?  You can see it applied to the true ROI of ad spending by taking the tutorial Comparing the
Potential Value of Customer Groups

Step by step instructions for creating future value and likelihood to respond scores for each customer, and for using these scores to create high ROI marketing campaigns and site designs are in the Drilling Down book.


    Home Page

Thanks for visiting the original Drilling Down web site!

The advice and discussion continue on the Marketing Productivity Blog
Twitter: @jimnovo

Read the first 9 chapters of the Drilling Down book: download PDF

Purchase Book



Slow connection?  Same content, less graphics, think Jakob Nielsen in Arial - Go to faster loading website

Contact me (Jim Novo) for questions or problems with anything on this web site.  

The Drilling Down Project.  All rights reserved, all media.



Ask Jim a Question


Get the book with Free scoring software at Booklocker.com

Find Out Specifically What is in the Book

Learn Customer Marketing Concepts and Metrics (site article list)


This is the original Drilling Down web site; the advice and discussion continue on the Marketing Productivity Blog and Twitter.

Download the first 9 chapters of the Drilling Down book here: PDF
Purchase Book          Consulting