By now, many online retailers should be familiar with the abbreviation “RFM,” which stands for recency, frequency, and monetary value. For a refresher, here’s my explanation from 2002.
Over the past several columns, I’ve examined conversion rate basics. This week, we continue our study of the basics with an updated look at RFM.
Recency represents the number of days since the customer last completed the action you’re profiling. Frequency represents the number of times the customer has completed this action since the first time she completed it. Monetary value represents the total value (usually total sales) the customer created by completing these actions.
The classic RFM model produces scores that rank customers relative to each other for the likelihood that they will repeat the action being profiled. Any action can be profiled: visits, purchases, logins, and so on. High likelihood to repeat an action, providing this action has economic value to the company, means high future value. Low likelihood to repeat means low future value. RFM is that simple.
RFM is a commonsense way of sorting marketing and optimization decisions based on what your visitors actually do and what they spend.
Let me defer that answer to a friend, optimization junkie, and fan of RFM. According to Frank Malsbenden, VP/GM of Vision Retailing, parent company of Shoeline.com:
The use of RFM metrics can drastically improve the effectiveness of e-mail marketing. It’s no secret that your best chance at repeat business is within 30 days of the customer’s last purchase. It’s not hard to grasp that frequency of purchases are a key metric in understanding the “health” of your customer database. And if you think about what you expect for service levels at retail establishments you spend heavily with, it’s easy to put yourself in the shoes of customers who spend big bucks with you. This is RFM, and catalog empires were built on the premise that recency, frequency, and monetary metrics should be the driving force in customer retention strategies…
But sometimes in e-commerce, we outthink ourselves, get too cute by half, or just get too plain confused by all the data that flies our way on a daily basis. Instead of keeping it simple, we get too “sophisticated.” What we should be doing is mastering techniques like RFM that have been proven over time. The possibilities with RFM and e-mail are endless. At Shoeline.com, we’ve started off with the basics. We developed e-mail templates for customer’s who purchased 30, 60, 90, 180, and 365 days ago. The content for each template is dynamically driven based on the RFM score of the recipient as well as references to previous purchases. We’ve developed the process so that these e-mails are sent automatically every day. All we have to do is change the content for the templates on a seasonal basis to ensure thematic relevance. The results speak for themselves. In 2008 the difference in open-conversion rate (orders/opens) was 31 percent higher for RFM-driven e-mails versus our traditionally segmented e-mails. The difference in straight conversion rate (orders/recipients) is so stark I’m afraid to go public, for fear of losing credibility. These results have prompted us to increase the percentage of RFM-based e-mails in 2009, and you can be assured that someday soon, 100 percent of our e-mails will be RFM-based.
Obviously the value of RFM goes beyond e-mail and can be used to increase SEM efficiency, ad targeting, and even merchandising. Still the simplest and most profitable use of RFM scoring is to identify and resell your best customers over and over.
Once you grasp RFM fundamentals, you’ll be inspired. Once you sort by these criteria, you’ll quickly find new and exciting ways to use them, such as:
- Is that PPC (define) campaign really bringing in high RFM customers or just low RFM customers? Any campaigns you would kill?
- What do high-scoring customers buy more of? Are there any patterns? Can you make them an offer to sell them again?
- What other behaviors are high RFM visitors engaging in on your site? Are average number of pages higher or lower? Is time on site higher? And so on.
- Create new KPIs (define) designed to optimize higher RFM visitors retention.
- Does RFM data give you any insight on merchandising and inventory?
My one tip when using RFM: don’t waste too many resources turning low RFMs into higher ones. It’s much more efficient to keep higher RFMs engaged.
If you need a quick immersion in RFM, again I highly suggest “Drilling Down: Turning Customer Data into Profits with a Spreadsheet” by good friend Jim Novo.
Have you learned anything interesting from employing RFM techniques? Let’s us know in the comments section what you’ve found.