Recency Frequency Monetary Modeling (RFM)
RFM analysis is a technique used to group or segment existing customers based on historic behavior in the hopes that history can, with the right motivators, be caused to repeat or even improve upon its self. The acronym is short for Recency, Frequency and Monetary value and each of these measures aligns to one or more of the three methods of increasing revenue for a business.
RFM is an effective process for marketing to your loyal customers and uses purchase behavior by recency, frequency and monetary to determine what offers work for what type of customers. Generally, only small percentages of customers respond to typical offers. But with RFM, you can ensure you are targeting the right set of customers who are most likely to respond. RFM is a powerful segmentation method for predicting customer response and ensures improvement in response as well as profits.
It is used primarily for targeted campaigning, customer acquisition, cross-sell, up-sell, retention, etc and is a guarantor of campaign effectiveness and optimization.
One of the most commonly used forms of segmentation is RFM (recency, frequency and monetary value). RFM is a good way to define and understand customer value. As well as helping customer development it can also form the basis of a good customer retention strategy.
Defining the Terms of RFM
Just what are “recency,” “frequency,” and “monetary” measures? The concepts are simple, even intuitive but turning them into measures that you can use to produce RFM scores can be somewhat tricky.
Keep in mind that the measures you use to rank your list are not the same numbers as the 5-4-3-2-1 score that you assign to each customer. For recency, you’ll figure out how long it’s been since each customer interacted, in days, weeks, or months. You then use those time-based measures to rank your list in order, from most recent to the long-lapsed. The recency score comes from that ranked list, with the 20 percent who gave most recently assigned a score of 5.For frequency, the measure is number of interactions in a given period. For monetary, the measure is total transaction value.
When did the customer last place an order, visit our store or interact with us in a material way? A customer who recently had a favorable interaction with our firm is, we hope, predisposed to repeating that interaction and thus susceptible to an offer that would encourage future business. Similarly a customer who hasn’t done business with us for sometime may be open to an offer of resumption that draws them back.
How many interactions, over a period of time, has the customer had with us? Assuming the interactions have been favorable for both parties, we would hope that we can sustain or increase the frequency of the interactions to our advantage. As with a customer who has not done business with us recently, frequency of interaction is a trigger you will want to pay attention to when it falls off over a period of time. This is where the frequency measure is often correlated to the recency one.
Over a given period of time, or number of interactions, what is the value of the customers business either in terms of revenue or profitability? Grouped in with monetary analysis is often inventory and channel analysis to get a sense of customers whose purchases reflect higher margin activities for the business such as buying large volumes through automated channels or the purchase of inventory items that have higher margins, are slow moving in various periods or are ends or remnants of other jobs.
Why does my business need RFM?
i) Catalog and direct-mail marketers were early adopters of RFM techniques to determine which customers got which catalogs, how often and with what special incentives, coupons or savings. With the advent of high capacity colour digital presses, many companies now custom print each catalog, varying the items on pages, prices for items and even specialized promotional offers for each customer based on the findings of RFM analysis.
ii) RFM analysis forms the basis of every customer loyalty program in operation from frequent flyer or hotel guest programs to retail shopper reward cards.
iii) If you’ve ever been to a casino you’ve seen RFM analysis combined with life-time value analysis. These are the principles upon which casinos issues complementary hotel rooms, meals, show tickets and everything else they offer “for free” to patrons of their establishments. Even the so called “free drinks” you can get in a casino are carefully distributed based on a real time size-up of your value to the casino based on RFM analysis.
Labels: Data Mining