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.
They’d 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 customer’s 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 it’s purchases, visits, sign-ups, surveys, games or anything else. I’m 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, doesn’t 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
Value (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.