RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries.
Recency - How recently did the customer purchase?
Frequency - How often do they purchase?
Monetary Value - How much do they spend?
Most businesses will keep data about customer purchases. All that is needed is a “table” with the customer name, date of purchase and purchase value. One methodology is to assign a scale of 1 to 10, whereby 10 is the maximum value and to stipulate a formula by which the data suits the scale. For example in a service based business you could have:
Recency = 10 - the number of months that have passed since the customer last purchased
Frequency = number of purchases in the last 12 months (maximum of 10)
Monetary = value of the highest order from a given customer (benchmarked against $10k)
Alternatively, one can create categories for each attribute. For instance, the “Recency” attribute might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be formed from applying business rules, or using a data mining technique, such as CHAID, to find meaningful breaks.
Once each of the attributes has appropriate categories defined, segments are created from the intersection of the values. If there were three categories for each attribute, then the resulting matrix would have twenty-seven possible combinations (one well-known commercial approach uses five bins per attributes, which yields 125 segments). Companies may also decide to collapse certain sub-segments, if the gradations appear too small to be useful. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value). Identifying the most valuable RFM segments can capitalize on chance relationships in the data used for this analysis. For this reason, it is highly recommended that another set of data be used to validate the results of the RFM segmentation process.
Besides being a simple technique with results readily understood by business people, critics take issue on several points. First, the method is descriptive only, and does not provide a mechanism to forecast behavior as a predictive model might. Second, when used to target customers for promotion, 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. Finally, when used as the primary targeting method, it may lead to overmarketing to the most attractive RFM segments and to neglect of other segments that would be profitable if developed properly.
A weakness of the RFM approach is apparent in Table 1, which describes the purchasing histories of two customers. Both customers have identical R, F and M scores, and yet the interpretation of those values suggests very different futures for the two. Other problems with the RFM approach appear when they are combined to form a single score.
For example, a recent publication described how an RFM score was calculated for customers making transactions in the past half-year. If their last purchase was in the first quarter of that half-year, R was assigned the value 1; if in the second quarter, R is set to 2. Frequency was set to the number of purchases in that half-year. M is the total revenue from that customer in the half-year period. The three numbers (R, F and M) were multiplied together to get the RFM score. Customers were then compared on the basis of their computed scores.
Clearly, this scheme has the desirable property that if two customers have any two of the three measures equal, then the one rating higher on the third measure will have the higher RFM score. However, when customers differ on more than one of these measures and trade-offs must be made between the R, F and M values, some very difficult-to-justify scores appear, as shown in Table 2 below.
There are many questionable comparisons contained in Table 2. Please note that between customers B and C, the “tradeoff” between M ($15 more by B) and F (one more purchase by C) favors C by 200 RFM points, whereas the three purchases by C for $230 rate 315 points above the single purchase of $375 by A. Also, between customers H and J, if H made the purchase on the last day of Quarter 1 and J made the purchase on the first day of Quarter 2, the difference in one day’s activity doubles the score for J.
Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organization. The resulting score is used to determine which leads a receiving function (e.g. sales, partners, teleprospecting) will engage, in order of priority.
The most accurate lead scoring models include both explicit and implicit information. Explicit scores are based on information provided by or about the prospect, for example - company size, industry segment, job title or geographic location. Implicit scores are derived from monitoring prospect behavior; examples of these include Web-site visits, whitepaper downloads or e-mail opens and clicks. A new type of score is the Social Score - it predicts lead relevancy based on analyzing a person's presence and activities on social networks.
Lead Scoring allows a business to customize a prospect's experience based on his or her buying stage and greatly improves the quality and "readiness" of leads that are delivered to sales organizations for followup.
Lead Scoring helps you automatically rate leads based on their real-time activity. Using objective data, these complex leads are categorized according to a scoring threshold—those falling below are placed into a nurture track, and a sales rep is automatically assigned to follow up.
Lead Scoring & Lead Nurturing are essential parts for Marketing Automation.
Examples of Scoring Rules
Direct marketers have been using recency, frequency, and monetary (RFM) analysis to predict customer behavior for more than 60 years. It’s one of the most powerful techniques available to a database marketer. Typically, you begin by sorting your customers by most recent purchase date, divide them into equal quintiles (20 percent in each) and assign numbers to the quintiles. The most recent 20 percent are 5, next 4, next 3, etc. You do the same thing by frequency of purchase and amount of spending.
You end up with a file of 125 exactly equal cells coded from 111 (responded a long time ago, and only once, and spent very little money) to 555 (very recent, very frequent and a high-dollar spender). You make money by sending your direct mail promotions only to those shown by RFM high numbers to be most likely to respond and buy. It works.
So why isn’t RFM a winner with email campaigns? It’s because the key value of RFM is telling you whom not to mail to so as to save postage. With email marketing, the delivery cost is much lower and can be ignored as a constraint on mailing a list. Those coded 111, who would be skipped in a direct mail promotion, could still produce profits from an email campaign. The cost of mailing them is so small that if even one of them buys something, it will probably pay for the emails to everyone in the 111 cell.
Despite this, hundreds of marketers who grew up in the direct mail industry are using RFM for email marketing. They look very professional with their RFM coding and response charts. What they cannot do, however, is show how RFM is making money over other ways of segmenting their house file. A better option than RFM is customer segmentation by the type of product they bought last or when they made their last purchase. Sending emails that contain dynamic personal content will get your emails opened. Using the customer’s previous history in your email will get that person to click on your links. If you know, for example, that some of your customers are college students, others are women with small children, and a third group are empty nesters over 55, and can create different content for each group, you may increase sales far more than any mailing based on RFM codes.
While traditional RFM isn’t ideal for email marketing, the concept of scoring buyers is already heavily in use, especially by B2B marketers who score prospects based on their firmagraphics and interaction with their website, emails, event attendance, content, etc.
Lead scoring is the next wave for B2C marketers as well.
What’s starting to be increasingly used today by savvy marketers are behavior-based triggers. In other words, instead of using a complex RFM model, marketers can use the essence of the “R” in RFM to identify a customer that’s engaged—e.g., abandoned a shopping cart, clicked on a specific product link in an email, or visited a specific product page on your website. Then you send them an automated, triggered message based on that action.
How Your open, click and conversion rates will go through the roof.
RFM Analysis can give you the customer purchasing behavior.
The purchasing behavior is the mother lode of the data mine. Once you do have past purchase history, you can make this richer by calculating RFM with it.
Your leads, of course, won’t have a past purchase history. But your other customer records will have this information and, it can be a major advantage in lead scoring.
RFM results will help you understand how to target special offers and promotions to different audiences and demographic groups more effectively. This will help you in Lead scoring and marketing automations campaigns.