RFM (Recency, Frequency, Monetary) and CLV (Customer Lifetime Value) are advanced customer segmentation models that allow eCommerce brands to identify their most valuable customers and tailor marketing efforts accordingly. By analyzing how recently a customer bought, how often they buy, and how much they spend, brands can predict future revenue and significantly improve their retention rates and marketing ROI.
What are RFM and CLV and how do they help eCommerce brands?
One of the most popular ways to segment your customers is by their purchasing behavior, namely recency, frequency, and monetary segmentation (RFM): how recent their last purchase was, how frequently they purchase from you, and the average price of their purchase. Perhaps most importantly, RFM segmentation can help you get the data needed to estimate a customer’s lifetime value (CLV), which is the monetary estimation of the value your business will derive from your relationship with any given customer.
As highlighted by HubSpot's guide to calculating Customer Lifetime Value, a series of simple math equations can tell you the CLV of any given customer based on their purchase frequency and average order value. To calculate average order value, divide your total annual revenue by the total number of orders placed in the past year.
Determining his or her purchase frequency is as easy as dividing the number of orders they have placed in the past year by the number of unique customers you’ve had in the past year.
Once you have these two variables, multiply them to find a customer’s lifetime value.
Taking CLV into account through RFM segmentation is important as it can help you increase customer value and loyalty, which I’ll explain in detail later, but it’s important to understand how to interpret these numbers first.
How do you calculate and interpret RFM and CLV data?
You now know how to calculate a customer’s lifetime value in the numerary sense, but what exactly do the numbers mean? It’s easy to break down RFM segmentation.
According to CleverTap's methodology on RFM analysis, Recency describes how recent a customer’s last transaction was, so low numbers mean they were more recent; for example, a ranking of 1 means his or her most recent order was received within the last day. Frequency is the opposite: higher numbers translate to a higher frequency, meaning a ranking of 1 would be an infrequent, one-time customer while a ranking of 24 would mean they have purchased two dozen times. Monetary is the easiest to understand, as the ranking translates directly to the average amount of money spent.
Analyzing customer lifetime value can appear to be a little more complicated, but in reality, it isn’t so difficult to understand. Completing the equations in the previous section will give you a number, and this number stands for the net profit attributed to your relationship with the customer in question. For example, if the customer’s average order value was $20,000 and their purchase frequency came out to .07, their customer lifetime value would be $1,400. This is the amount you could expect to make from that customer throughout your relationship based on their past buying behavior.
How can RFM segmentation increase customer lifetime value and loyalty?
Although the process can be painstaking, segmenting your customers based on buying behavior and purchasing habits and calculating customer lifetime value allows you to more easily increase customer value and loyalty. Dividing a large customer base into smaller groups based on their buying habits helps you to identify your target audience and understand different kinds of customers’ wants, needs, and key motivators.
Additionally, segmenting customers into more manageable groups allows you to personalize content and marketing materials. As proven by McKinsey's research on personalization, tailored outreach can significantly improve your conversion and retention rates as well as customer loyalty. This is simply because generic, impersonal marketing materials are less appealing to customers than something personal and relevant to their behavior and preferences.
Which eCommerce companies have successfully used RFM and CLV?
Don’t just take my word for it - RFM segmentation and calculation of CLV data has helped a number of major companies increase customer loyalty and revenue. Eastwood, L’Occitane, and Frederick’s of Hollywood have all used this type of segmentation and analysis to kickstart their brands and establish them as household names.
Eastwood’s email marketing profits soared by over 20%, L’Occitane saw a 2,500% increase in revenue through email, and Frederick’s of Hollywood increased their conversion rates by nearly 10%. Eastwood’s case in particular is interesting as their analysis revealed that nearly half of their engagement came from the 4% of its customers with the highest RFM rankings. Their findings led them to send daily emails to this small segment of loyal customers, and, amazingly, the frequency with which they were clicked through increased rapidly while the number of opt-outs and spam complaints dropped.
What are the risks of over-segmenting your customers?
Although RFM segmentation and the calculation of customer lifetime value can do wonders for a brand, the short answer is that, yes, customer segmentation can backfire if you overdo it. Over-segmentation is a real risk when trying to figure out how best to divide up your customer base as it could lead to the accidental exclusion of potential customers.
Additionally, over-segmenting your customers can cost money and waste time. If you develop 12 buyer personas and later realize that half of these personas overlap and could have been consolidated, you’ll regret the hours you spent trying to define specific segments. To avoid this, stick to larger segments.
Another danger of over-segmentation is over-personalization. For example, say your company has a segment of customers that tend to spend the highest amounts of money per purchase out of any segment you’ve defined. You could send them personalized messages and notifications about your most expensive items or services to cater to their past buying behavior and purchase history, but you’d be neglecting to advertise other less expensive products that may appeal to these customers. In other words, if you fixate too much on what you know about consumers based on their past behavior, you may be creating echo chambers for members of your audience.
Conclusion
Despite these risks, RFM segmentation and CLV analysis can transform your business by reinvigorating your relationships with your customers. By identifying your most valuable segments and engaging them with tailored, relevant content, you can drive long-term revenue and build sustainable brand loyalty. To learn more about how data can shape your eCommerce strategy, make sure to check out our other guides and explore tools like StackTome to automate your review and retention pipelines.
