Recency, Frequency, Monetary are foundations of customer segmentation. Few realize that just having data is not equal to having something useful. This short video discusses a common method for scoring Frequency and a much better alternative.
- The annual repurchase rate of reactivation candidates at the margin is only about 7% in this example. When repurchase rates are low, RFM is competitive.
- The organic percentage is about 40%. So if the annual repurchase rate is just 7% at the margin, and 40% of the 7% will happen with/without aid of catalogs, then the effective annual repurchase rate is actually (1 – 0.40)*0.07 = 4.2%. A lower effective annual repurchase rate makes RFM more competitive.
Customer Segments Part (1 of 3)
An overview of RFM. Recency, Frequency; Monetary (RFM) continue to form the basis of many behavior analysis variables. Learn about the Direct Marketing basics of RFM in this series of videos.
Watch Out with RFM (2 of 3)
RFM Revisited – learn about the real-world danger in committing too completely to this simple formula.
RFM Secrets (3 of 3)
Recency, Frequency, Monetary value. The calculation of scores based on these three transactional variables has a lot of sway over the minds of fundraisers, and I just don’t understand why.
It’s one of those concepts that never seems to go away. Everyone wants a simple way to whip up an RFM score. Yet anyone who can do a good job of RFM is probably capable of doing real predictive modeling. The persistence of RFM seems to rest on some misconceptions, which I want to address today.
First, people are under the impression that RFM is cutting-edge. It isn’t. In his book, “Fundraising Analytics: Using Data to Guide Strategy,” Joshua Birkholz points out that RFM is “one of the most common measures of customer value in corporate America.” It’s been around a long time. That alone doesn’t mean it’s invalid — it just isn’t anything new, even in fundraising.
Second, it’s often misconstrued as a predictive tool, and therefore the best way to segment a prospect pool. It’s not. As Josh makes clear in his book, RFM has always been a measure of customer value. It does not take personal affinities into account, nor any non-purchasing activities, he writes.
Note the language. RFM is borrowed from the for-profit world: retail and sales. Again, this doesn’t discredit it, but it does make it inappropriate as the sole or primary tool for prediction. Because it’s purely transactional in nature, all RFM can tell you is that donors will become donors. It CAN’T tell you which non-donors are most likely to be acquired as new donors. The RFM score for a non-donor is always ZERO.
It also can’t tell you which lapsed donors are most likely to be reactivated, or which donors are most likely to be upgraded to higher levels of giving. In the eyes of RFM, one person who gave $50 last year is exactly the same as any other person who gave $50 last year. They’re NOT.
Third, we’re often told that RFM is easy to do. RFM is easy to explain and understand. It’s not necessarily easy to do. Recency and Monetary Value are straightforward, but Frequency requires a number of steps and calculations, and you’re probably not going to do it in Excel. Josh himself says it’s easy to do, but the demonstration in his book requires SPSS. If you’re using a statistics software package such as SPSS and you’ve mastered Frequency, then true predictive modeling is easily within your grasp. Almost all the variables I use in my models are simpler to derive than Frequency.
Is RFM useless? No, but we need to learn not to pick up a hammer when what we really need is a saw. RFM is for ranking existing donors according to their value to your organization, based on their past history of giving. Predictive modeling is for predicting (who knew?), and answering the three hard questions I listed above (acquisition, reactivation, upgrade potential.)
You could, in fact, use both. Your predictive model might identify the top 10% of your donor constituency who are most likely to renew, while your RFM score set will inform you who in that top 10% have the highest potential value to your organization. A matrix with affinity (predictive model) on one axis and value on the other (RFM) would make a powerful tool for, say, segmenting an Annual Giving donor pool for Leadership giving potential. Just focus on the quadrant of donors who have high scores for both affinity and value.
If you want to use RFM in that way (that is, properly), then fill your boots. I recommend Josh Birkholz’s book, because he lays it out very clearly.
The real danger in RFM is that it can become an excuse for not collecting more and better data.
For any institution of higher education, the idea that RFM is the bee’s knees is patently untrue. Institutions with alumni have a trove of variables that are informative about ALL of their constituency, not just those who happen to be donors. Expand that to arts-based nonprofits, and you’ll find member-based constituencies and the very same opportunities to model in a donor/non-donor environment. Neither of these types of institutions should be encouraged to rely exclusively on RFM.
For the rest, who don’t have the data for their constituency but could, the idea that pure donor transaction data is all you need cuts off the chance of doing the work now to get more sophisticated about collecting and organizing data that will pay off in the years ahead.
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.
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.
The key in the comparison is to find a method which isn’t too cumbersome, is easy to understand and perhaps most important is fair to both techniques. Both RFM and segmentation models attempt to predictively rank customers from good to bad. We need to keep not only the results but their interpretation in mind as we compare. There are three common approaches: random allocation, unique comparison and ID match.
Random allocation involves matching the customers selected by RFM with the customers selected by the model. Two tapes are created, many names happen to be common. Using an exact match (the names came from exactly the same source) the two tapes are compared at your service bureau. The matching names are then randomly reallocated first to one tape, then the next match to the other tape. In this way, each segment retains its original key code. Unfortunately, the segments with the most matches lose some of their original count. At the same time, it is fair to both techniques. Assuming both tapes had about the same total number of names, each had the same number of matches, therefore, we can simply compare total sales and profit and see which technique did the best.
This method preserves the complete original key coding of both tapes. If you don’t have equal quantities you can still easily compare the two results. Assuming you have an expected rank of your RFM cells, it is a good idea to line up results with quantities as the X axis and sales or profit on the Y axis. This graph illustrates the dramatic difference between RFM and modeling results.
Compare Uniques is probably the easiest both to do and to understand. Simply match the two tapes, preserve the RFM key codes for the matched names. Key the unique names of each tape separately. The assumption is that if the new technique finds names RFM would have missed and the names do better, then the model will more than pay for itself. Mailers who care mostly about “beating control” focus on this most common method. In a recent test, our model uniques generated $15 sales per catalog, the RFM uniques generated $1 per book. A great win “right?”
This technique separates the unique names from their original segments. Little insight is gained by the comparison. Further, to the extent that RFM is usually pretty good at finding the best names, there might not be much difference at the top end. The uniques that are compared often lie far down into the moderate or poor customers. Bottom line, this technique tends to destroy analysis of the results. This overly simplistic comparison reduces the subtleties of both RFM and statistical segmentation to a horse race. A few months ago, a client may have manipulated this comparison by first selecting all the 24 month buyers, then they required selection of modeled names far down into the poorer names. The net effect was to force a comparison between their unique RFM names the model missed with the forced uniques way down in the heap.
ID Match requires some computer processing to understand what happened in the mail. Two tapes are created. Customer ID’s and key codes are preserved. When orders come in, they are matched back to each tape. In this way, results actually recreate how both mailings would have done. Most importantly, this technique perfectly preserves both customer rankings and cell counts. Assuming we again compare sales and profit as mail quantities increase, we can lay both techniques side by side as seen in the graph above.
If you are planning to spend the money to tip toe into modeling, make sure you also take the time to plan your testing and evaluation techniques. Comparing uniques might be the most common way to assess payback, but the other two provide a much richer picture of what is really going on in your customer file. Good luck
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.
Basic RFM Scoring
To build a simple RFM score, take a few thousand customers (or more if you have them) and their orders and put them into dBASE. Next, query all the orders less than 3 months old. With a join, you can link the orders to customers and use CALC_AS to give them a 5. We typically give <3 mos. a 5, 3-6 mos. a 4, 6-12 mos. a 3, 12-24 mos. a 2 and >24 mos. a 1. (A little tip: if you start at the low end, you can be sure that you have all the customers properly scored even if they have several orders).
For frequency and monetary, it is best to first build fields with each customer’s life to date history. Query and Calc_sum the orders and dollars for each customer. We use an interesting scoring technique I learned from John Wirth, President of Woodworker’s Supply. We total all the orders and sort the customer by number of orders so our best customer would be first. We give them a 5 in recency score. We then subtract that customer’s number of orders from the total. When the total reaches 80% of the original, we start giving those customers a 4. This gives us five breaks (actually six because non-buyers get 0) of the number of orders focusing our F and M on those customers who are most profitable. If modeling is based on uncovering the “80/20″ rule, this method forces you to concentrate more on the top 20.
If you want to validate the impact of RFM on your file, use the above techniques but use only orders up to six months ago. Once your file is scored, look at the orders from customers in each cell. You will be looking at customers as they would have been selected if you would have used RFM. Of course you will find that those in the highest cells will dramatically out perform the rest.
When we began building complex models, we talked to a seasoned Ph.D. about how to tell whether what we were doing was right. He replied, “If you don’t see recency at the top, you better recheck.” This statement illustrates both the strength and weakness of RFM.
RFM tends to be applied in a vacuum, ignoring other important information. I ran a successful business-to-business catalog for several years only using recency. We felt that if someone hadn’t responded in four years, they weren’t worth mailing. Our file was small and this approach was working. Looking back, I wish I would have considered additional information.
If you have broad product lines and sales varies through the year, then you probably have very different customers buying in different seasons. If we ask, “Who will likely buy?” Recency always wins. If, instead, we ask, “Who is likely to buy ‘X’?” we will probably get a very different answer.
It is intuitively obvious that men are different than women, young different than old, etc. It is also plain that customers who buy one type of product do not necessarily buy all your other products. Further, if the products suggest a lifestyle (like woodworking, fishing, needle point or other interests) when you examine who is likely to buy, recency can almost dissapear!
The fundamental issue is not whether RFM works, but whether we are asking the correct question. I would contend that building one model for travel buyers when some go to the Yukon and others to Cancun is odd indeed. My statistics friend (who banked on recency) assured me that it is always better to build a model based on your specific offer. “Of course! If you want modelling to work, you should almost never have just ONE model.” he replied. But because of the cost, if a mailer has a model, they probably have only ONE.
One of the great advantages of RFM is its simplicity. This is also its great weakness. Some score frequency by dividing their customers into equal groups of say 20%. This gives you one cell that covers the top 20% of your customers (probably 80% of your sales) and four cells for the bottom 20% of your sales. Above, we suggested a fix for this, but at the same time, we then create a bottom cell that may include 50% of your customers. It is very common to find that over half of your customers have ordered once, spent under $100 and haven’t ordered in two or more years.
It is not difficult to identify and contact your best customers… they are the ones who call the most already. If you do nothing more than put offers in your out-going packages and trigger an additional follow up mailing one month after the shipment, you will be getting to the right people. You don’t need a model for this! Virtually all of the articles on RFM imply that segmentation will more than pay for itself by saving wasted circulation. RFM must focus on your best customers. The problem is not who is your best customer, but who of your not so hot customers is worth mailing? We should also note that RFM will have little value in prospecting. Obviously, if you are looking for new customers, they will not have any RFM values.
Two obvious solutions arise: First, include product category in your analysis. Second add information external to buying behavior (geodemographic information on the type of neighborhood a customer lives in).
Their are many ways to add product (“P” in RFMP). The simplest is to trigger a flag the first time a customer buys something in a category, in addition, you can track the sales history by category and score it like monetary. Many use a heirarchical structure which makes it easy to select names; this is the worst for modeling. If you are mailing a special offer, you can now break those poor customers (low RFM scores) into those who have purchased or not purchased the relevant product categories.
Neighborhood information also allows you to break up the weak cells. Even if all you knew was who lived in an urban, suburban or rural setting with high, medium or low income… you could obviously improve your ability to mail into the low cells.
RFM can easily lead to smaller and smaller customer mailings. Short term, profits improve; long term, the warehouse gets too big. It is important to look beyond the simple question of “Who is most likely to respond?” Additional variables help us identify the possibility of mailable customers within poor RFM cells.
John Miglautsch is President of Miglautsch Marketing, Inc. He can be reached at firstname.lastname@example.org.