There are a lot of different approaches businesses take when trying to assess the value of leads and customers. Quite frankly, a lot of these approaches are wrong, or at the very least, they end up being incredibly inaccurate.
One approach that all businesses should factor into the equation is RFM Analysis, which takes a number of core factors (recency, frequency, and monetary value) into account when assessing a client’s value.
RFM analysis is not perfect, and there’s been a lot of debate and even some myths surrounding its effectiveness, use cases, and impacts.
So can RFM analysis work for you and your business? We say yes, and that it can be a valuable part of lead assessment when you know how to do it right.
Let’s take a look at exactly how RFM analysis works, why it can be valuable, and a few core myths to write off.
- What is RFM Analysis?
- The Potential Pitfalls of RFM Analysis
- 3 RFM Analysis Myths You Shouldn’t Fall For
- Final Thoughts: How Do I Apply RFM Analysis to My Business?
What is RFM Analysis?
Before we move on to debunking RFM Analysis myths, it’s essential to take a step back and understand what RFM really means.
RFM is an acronym used by marketers that stands for the three following things:
- Recency–How recently was a purchase made?
- Frequency–How often has a customer made purchases in the past?
- Monetary Value–How much did the customer spend on the last order or on their average order?
These three essential items are widely regarded as the most important factors when trying to predict the value of a customer.
It only takes a few Google searches to find more than enough research to support how Recency and Frequency are significant indicators of purchase intent. Here are some of the most interesting ones to save you a few clicks:
- Most B2B buyers are already 57% halfway through the buying process before meeting with a representative. (Accenture, 2018)
- 90% of B2B buyers now twist and turn through the sales funnel, looping back and repeating at least one or more tasks in the buyer’s journey. (CMO, 2019)
- The average B2B buyer journey involves consuming 13 pieces of content, with an average of eight vendor-created pieces and five from third parties. (FocusVision)
This means customers who get the highest possible scores for shopping most recently, shopping frequently, and spending the most money would be your best customers.
By using an RFM analysis model to analyze your customers, you can also answer pressing questions like:
- Which customers are most likely to be the best fit for your business?
- Which customers do not fit your ideal target market?
- What are the key metrics for high-quality customers?
You can also use parts of the RFM analysis to assess leads who haven’t yet purchased with your business, even if you’re taking a slight deviation of the model and you aren’t looking at purchase values just yet.
- How recently did customers sign up for your free trial or demo?
- How often are users engaging with your emails or marketing messages?
- What high-value actions are users taking, like signing in to the trial daily or scheduling a sales or onboarding call, and how could that indicate value as a client?
It’s easy to discover RFM analysis and to have that all-power lightbulb moment. And we know that you feel like you’ve struck gold and are ready to hit the ground running, diving deep into your RFM analysis.
Before you do, though, it’s important to address a few crucial issues that are associated with the model, especially when used incorrectly.
The Potential Pitfalls of RFM Analysis
There’s no getting around the fact that recency, frequency, and monetary value are incredibly high indicators of value. They’re really some of the highest indicators, which is why we take all three into account with our lead scoring software.
Some businesses, however, make the mistake of exclusively using RFM analysis to assess client value both short-term and in the future.
Recency, frequency, and monetary value are not the only indicators of value that you need to be looking at. You also need to look at different types of actions taken and the fit of the customers (including industry, size, use case, and revenue), too.
These matter greatly whether you’re trying to identify new high-value leads or find high-value clients.
Customers who keep hitting the limit of their existing plan, for example, are prime candidates for upselling—neglecting these customers as having lower monetary value when they could increase their average monthly order is a mistake.
It’s also important to consider that many lead scoring tools on the market don’t allow you to factor recency and frequency data into their models; they may allow you to factor in one, but often not both.
That’s just one thing that does set us apart here at Breadcrumbs—we account for both fit and activity, and all activities are measured with frequency and recency in mind to really help you get a good read and accurate contact score on any user at any point in time.
3 RFM Analysis Myths You Shouldn’t Fall For
There are three common RFM analysis myths that you’ll frequently hear when the topic is discussed. Let’s take a look at each one.
RFM Analysis Myth #1: Lead scoring is outdated or ineffectual
If you’ve been in the marketing realm long enough, I’m sure you’ve heard this phrase: “We should all be trying to sell to everyone that hits our website!”
The idea is that every user could be a customer worth pursuing.
That couldn’t be further from the truth. While it would be great to sell to every user that visits the site, the reality is that a solid chunk just won’t be a good fit. More importantly, it’s extremely difficult to attempt to do it while scaling cost-effectively.
Plenty of research shows that getting to leads in a timely fashion increases the chance that you’ll win the deal. This is where recency comes into play, and it’s why some brands market heavily towards any users who visit their site. That being said, that’s only true when we’re talking about legitimate sales leads that are already well into their buyer’s journey and on their way to purchase.
In fact, according to Forrester, 60 percent of B2B buyers are only ready to talk to a salesperson once they’ve done their research and are well into their buyer’s journey.
Many marketers succumb to the “every visitor is a customer” pressure, so everything becomes a lead resulting in only 27% becoming qualified.
As a result, some lead scoring models that only look at recency and maybe an activity or two are causing people to write off lead scoring tools as ineffectual. This is because most lead scoring tools do NOT account for RFM analysis, and they certainly don’t look at both fit and activity.
Outdated lead scoring tools are outdated. Breadcrumbs, however, takes all of the above into consideration to give you a more accurate understanding of which customers are most likely to convert so your sales team can act when recency and frequency are high—and when the lead is actually a great fit.
Want to see Breadcrumbs lead scoring in action? Create your free account today!
RFM Analysis Myth #2: MQL’s aren’t worth the sales team’s time
MQLs (or Marketing Qualified Leads) get ignored because sales teams don’t trust marketing. They think—and sometimes not unfairly—that marketing departments can throw spaghetti at the wall to see what sticks and think all leads are good leads.
You’ve probably heard this said before, too; it’s a massive part of the reason why there exists an air of tension between the marketing and sales teams.
After all, why should the sales team spend their hard-earned time calling every lead when only 30% of the people are ready to buy?
Research by InsideView found that Sales Reps consistently ask for “better quality leads” and, ideally, more of them from their marketing teams. When you consider that companies that use lead scoring to qualify leads see a 77% greater marketing ROI, but only 44% of companies use lead scoring systems, it’s not hard to see why.
If you take this information into account, it’s not hard to understand why marketing and sales alignment is a consistent boardroom topic.
MQLs, however, can absolutely be worth the sales teams’ time—and they should not be writing them off.
When the sales and marketing teams work together so that the marketing team has the information they need to drive more high-quality leads, that’s a good start.
And with proper lead scoring techniques in place, it will be easier for your sales team to assess the quality of MQLs and product-qualified leads (PQLs) to determine which are worth their time.
RFM Analysis Myth #3: If we don’t get in front of our leads first, then we’ve already lost
The vast majority of your future customers are on a twisting and turning buying journey.
Most also don’t want to engage with a representative as their primary source of information when they’re making a buying decision.
Consumers are smarter than ever, and buyers (both B2C and B2B) are researching more and taking a longer time before purchasing.
This means it’s crucial to identify a buyer’s identity to sell more and to create better deals.
Timing is everything, but making sure you are timing the right thing is key–engaging with your real future customers should be your main goal.
The battle isn’t lost just because you don’t get a chance to engage with users first. What matters is that you engage consistently, as quickly as possible upon them reaching out to you, and in a personalized way to better convert the users.
Final Thoughts: How Do I Apply RFM Analysis to My Business?
So what does it look like to apply the principles of RFM to your B2B business?
Well, at a minimum, it looks like this:
- Identify the most common combination of behaviors and attributes of your ideal customers
- Make sure that both sales and marketing agree with your definition of “ideal customer profile” and their identifiers
- Apply Recency and Frequency scores to all the trackable behaviors identified
- Validate the predictive value of those identifiers by looking at data broken out by won vs. lost and elapsed time
If you’ve found meaningful clusters and are confident with your definition of an ideal customer, you have the building blocks of an RFM-inspired scoring model.
The big challenge we alluded to above, and one of Breadcrumbs’ main differences, is that baking Recency and Frequency into scoring models using traditional tools is tough.
We can deliver that, and our models all account for recency, frequency, fit, and activity with different model templates that are all entirely customizable.
Want to learn more about how Breadcrumbs works? Book your demo here.