The principal obstacle to effective database marketing is the development of profitable strategies for use of the database. It is relatively easy to construct a workable marketing database. Many service bureaus are experienced at this work and can do a very satisfactory job. What the service bureau normally cannot help you with, however, is figuring out how to make your database pay off. These strategies you will have to work out yourself.

One of the oldest, and still one of the best techniques, is Recency, Frequency, Monetary (RFM) Analysis. Using this method, any marketer with a large customer database can almost guarantee profitable promotions to his customer base time after time after time. This article explains how to code your database for RFM, the theory underlying it, and some practical examples of how to make it actually pay off.

Continue reading “Making Your Database Pay Off Using Recency Frequency and Monetary Analysis by Arthur Middleton Hughes”

This article was written after this article ran describing how “predictive modeling techniques outperformed Recency-Frequency-Monetary value (RFM) targeting in a back-to-school campaign.”  I received a ton of e-mail asking for an explanation of this confusing claim.

For those of you not well versed in what behavioral modeling is all about, this article provides a look inside and addresses some very Frequently Asked Questions on modeling.

For those looking for some resolution on issues brought up in the DM news article, I decided to just write this response and point all the queries to it (saves much typing!).  Thanks to all the fellow Drillers out there who thought there was something a bit off in this article.

Continue reading “RFM versus Predictive Modeling by Jim Novo”

RFM Basics

Direct marketing is fundamentally the scientific control of customer acquisition and contact.  The recurring question is whether Customer A merits an additional contact based on his past purchase behavior. This question applies equally to direct mail, catalog, phone, field or Internet contact.

The process of making this decision is customer segmentation. Not all customers have purchased identical amounts. Some have ordered more often, some have ordered more recently. Consequently, not all customers should be contacted with the same effort and expense. The cornerstone of direct marketing segmentation is RFM (Recency, Frequency and Monetary

values).

Since direct marketing segmentation is a science, it is important to quantify customer behavior so that we can test the short and long term effect of our segmentation formulae. The purpose of RFM is to provide a simple framework for quantifying that customer behavior. Once customers

are assigned RFM behavior scores, they can be grouped into segments and their subsequent profitability analyzed. This profitability analysis then forms the basis for future customer contact frequency decisions.

RFM Scoring

The purpose of RFM scoring is to project future behavior (driving better segmentation decisions). In order to allow projection, it is important to translate the customer behavior into numbers which can be used through time.

(read entire .pdf of the original article)

Why Use RFM?

RFM is short for Recency, Frequency and Monetary. These three variables when applied to a customer file become the backbone of mailing segmentation. Recency is based on when the most recent purchase was made. Frequency relates to the entire number of purchases made in a customers life-to-date. Monetary is the total money spent.

For any given mailer, a very small percentage of the customers spend most of the money. This is best captured by the Pareto Principle (80/20 Rule). 20% of your customers spend 80% of the money. As your customer file ages, it becomes less and less productive to mail all your customers. To identify the best 20% (or whatever proportion of your customer file is productive to mail/contact) we apply scores to your customers, produce reports and allow you to select as deeply as you feel appropriate. As results are generated, mailings become fine tuned with circulation increasing in the best seasons and decreasing when business is traditionally slower. Your circulation plan thus maximizes both sales and profits.

“To be useful, recency, frequency and monetary value must evolve hour by hour, day by day, week by week, month by month, quarter by quarter, and year by year. The value lies in the ability to see change over time; indeed, that may be the only value as it becomes a replicable measurement of consistently improving profitability created from increasingly better decisions.” Donald R. Libey, Author of Libey on Recency, Frequency, and Monetary Value (The Libey New Century Library).

So you’ve made the decision to try statistical segmentation or modeling against your traditional RFM. To cut through all of the hype, you vow to carefully test the new method against the old. But we’re not talking about an A/B split of one list, or measuring one rental against another. You will be comparing two different approaches to your customers. So what is the best way to make this comparison?

The first point is not to get too fine in your mailings. You won’t find anything out about customers you don’t mail. Use a mailing where you would ordinarily go pretty far down into your RFM schema. You also want to mail pretty far into the model. Even without mailing every cell, you might want to take a few thousand from even the lowest cells just to see what would have happened. Again, you don’t want to bet the farm, but if you are mailing most of your customers anyway, then the negative risk is minimal.

Continue reading “RFM vs. Models by John Miglautsch”

Ok, you’ve made the plunge, bought a model or modelling system. How do you begin to tell if it works as good as the ads promise?

There are three simple ways to compare your results. Each have strengths and weakness

We built a database for Bullock & Jones, a small upscale menswear catalog based in San Francisco. The modeling process takes about 4 hours for a small catalog. This compares with 4-12 weeks using leading service bureaus. The model projected a 40:1 gain. This is virtually unheard of, 5:1 being considered enough to cost justify the modeling process.

We put the model up against their regular mailing. We found a few thousand names that their RFM overlooked. They generated 3x average dollar per book of additional sales. We were also able to break up their 0-12 month buyers into better cells. This generated a much more dynamic picture of where to consider reducing circulation.

The graph illustrates the difference between interpreting the results of an RFM mailing and a modeled mailing. We can see that the model very successfully predicted the best cells (far left) and the worst cells (far right). You can also see that the slope is much more dramatic on the modeled mailing. This means that even inexperienced circulation planners are likely to achieve excellent results with the system.

Many people have contacted me this month about the idea that it is possible to expand on RFM segmentation (Recency, Frequency and Monetary). We have lightly touched on RFM modeling and suggested that this is only a first step in building segmentation models. Perhaps it is best that we begin by elaborating on RFM, illustrate some of its short comings then outline how we can expand the number of interesting analysis variables.

When I mentioned the increasing interest in RFM, one statistical friend of mine replied, “Isn’t that sort of rediscovering ‘70’s technology?” Though RFM has been around for decades it has not been widely applied. We went from the booming ‘70’s through the growth ‘80’s and into the downsizing ‘90’s. Most of us were so busy trying to keep up with the explosion, we didn’t really worry about exactly how to fine tune.

Continue reading “Inside RFM Segmentation Modeling by John Miglautsch”