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Magento Report – Recency, Frequency, Monetary

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.

So what exactly are the variables that actually make up the RFM score? Here are the three inputs:

  • 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.
  • 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.
  • 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 can this help the marketing team make better predictions about the future spend of its newest customers?

A marketer can divide the customer base into quartiles on each of these dimensions - for example, the top third, middle third, and bottom third based on transaction frequency - and find the historical CLV for each RFM "cell." For example, what's the average two-year spend of a customer who's in the top third by recency, the middle third by frequency, and the bottom third by average order size? By then situating new customers within each of these RFM cells based on their behavior to date, the marketer can come up with much more accurate CLV predictions.

Final Thoughts

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 among 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)?

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