So you’ve put together your first email newsletter, perfected the message content, and triple-checked your subject line. You hit Send and the email goes off to the entire subscriber base – it’s definitely going to lead to a 95% open rate, right?
If you’re like most marketers, then you’ll quickly realize that the average open rate for most email newsletters is usually less than 25%. And that’s okay – the reality is, whether you’re sending an email newsletter, or making phone calls, or posting a link on Facebook, only a small subset of your audience will engage at any given point. The lesson that many of us learn is not to tailor our marketing around reaching 100% of a hypothetical potential audience, but to target the individuals who actually engage with your business. But how do you identify these individuals who interact with your business? Is there a way to quantitatively measure this, beyond just remembering the names of our favorite customers?
To get you thinking about identifying strong engagement and finding customers who are likely to purchase in the future, today we’re going to tell you a bit about the RFM model.
The Recency, Frequency, & Monetary (RFM) Model is a classic analytics and segmentation tool for identifying your best customers. At its most fundamental level, it hypothesizes that customers who 1) have made a purchase recently, 2) make regular or frequent purchases with you, and 3) spend a large amount with you, are more likely to respond positively to future engagement and product offers. This might seem intuitively obvious to those of us who have experience in sales – but what the RFM model brings to the table is a framework for objectively measuring these three ideas on a numerical scale.
RFM was first developed many years ago for the direct mailing industry, where marketers would use it to decide the sort of mail catalogues that different families would receive. For instance, some families would receive catalogues that showcased high-priced items, while others received inexpensive ones, and some wouldn’t even get a catalogue – all based on how they scored on a set of 3 variables as defined by the RFM. Some 30 years later, the RFM model remains a useful method for optimizing your sales and marketing spend, as well as thinking strategically about how to engage your customers. (For those who are interested, Jim Novo has a great story on how RFM scores first emerged in the direct mail industry).
So what exactly are the variables that actually make up the RFM score? Here are the three inputs:
1) Recency: When was the last time a customer made a purchase order with your business? According to the RFM model, a customer who has recently interacted with your store is more inclined to accept another interaction that you initiate.
2) Frequency: How regularly does this customer make a purchase with your business? Over time, the frequency of a purchase can, in most cases, predict the likelihood and schedule of future purchases.
3) Monetary Value: How much money does this customer spend over a period of time? Depending on what makes more sense for your company, you might decide to calculate monetary value as revenue or profitability. Either way, this input adds monetary spend into the equation to calculate your RFM.
Put together, customers are then given a score that represents their three ratings across the inputs. The higher the RFM score, the more likely this customer will purchase from you again.
How RFM Impacts Your Business
With the widespread growth of digital marketing tools like email newsletters and blogs, businesses now have access to ever-growing customer lists and new ways of reaching them. While this of course presents many potential opportunities for growing your sales, a “one-size-fits-all” approach to your outreach will still waste many marketing dollars on customers who aren’t likely to purchase at this time. Segmentation tools like the RFM model are therefore useful for ensuring that your marketing dollars are spent on the right customers at the right time.
The RFM model can inform you on what level of service and attention you should be offering to different customers. For example, if you have a set of customers who haven’t been active for many months (in other words, scoring low on Recency), then maybe four information-heavy emails in the span of a week isn’t the best way to re-engage them (or maybe it is – it’s worth testing it out!). But the key insight that RFM can tell you is how to reach out to different customers based on whether they’re new patrons, returning customers, or something in between.
Similarly, charting a customer’s frequency score can tell you a bit about how often you should be reaching out of them. For instance, if you have a set of customers who only make two big purchases a year, then sending them a weekly email has the potential of becoming a source of frustration – and might even lead them to unsubscribe from your mailing list. So frequency scores are important to keep in mind in your outreach efforts as well.
On Monetary Value
This is an easy one: charting your customers based on monetary value helps you to identify the potentially big clients for your business. Armed with this knowledge, you’ll know which customers you should be reaching out to first!
Don’t forget: the RFM model gives you a historic picture of what your customers are like, but it’s also a great indicator of what your next objectives should be as well! For instance, if you notice a set of customers who score high on the monetary variable, but low in frequency and recency ratings, then you should be planning new ways to bring them back to your business in the future. Furthermore, are there certain traits or behaviors amongst these shoppers that you can identify and respond to in the future (for instance, maybe they seem to be business travelers, or happen to be younger patrons, or older ones)?
Nonetheless, RFM models still have their limitations. Firstly, the model only relies on three basic (albeit important) variables, so it could potentially be excluding other factors that help to predict future customer purchases as well. Additionally, businesses need to remember that customers with high RFM scores will still be turned off by an overload of sales and marketing materials. Similarly, customers with a low RFM score should not be viewed as leads that you can ignore, but simply individuals who have the potential to become better customers if you find out how to serve them!
Ready to see an RFM model at work? To give you a chance at inputting your customer RFM scores, here at Canopy Labs we’ve built a free calculator to show you how different variables and inputs will affect RFM scores. Try out our calculator here!
Learn more about RFM
The Intro Guide to the RFM Model is brought to you by Canopy Labs, a predictive analytics company. We work with businesses of all sizes to better understand – and sell to – their audiences. If you have questions about this guide or are interested in learning more about customer analytics, feel free to get in touch!
If you enjoyed this introduction to RFM and want to learn more, here are a few links that might interest you!