the Best-Kept Secret for B2B Product Sellers

Do you sell physical products to other businesses? Recency, frequency, and monetary value (RFM) analysis is a technique used by businesses worldwide, and it is an incredibly useful strategy for growing B2B wholesalers and distributors. 

Knowing how to calculate it across your entire customer base and how to action RFM insights can be tricky, particularly for smaller businesses. Yet, for product sellers, repeat orders are essential to profitable success.

In this article, we explain what RFM is and why you should care, how to calculate it, and how you can use RFM insights to create predictable, repeatable, and scalable success in your product business.

RFM is an industry strategy for segmenting customers using data you already have. This analysis technique assesses customer spend patterns across three areas: recency, frequency, and monetary value.

It’s well-used by big companies but often ignored by SMEs. Smaller companies typically have the necessary data, but understanding and calculating RFM seems complex and daunting. It doesn’t need to be! The principles are logical and easy to understand, and modern technology makes the calculation significantly more approachable for SMEs on tighter budgets because hiring expensive consultants or data analysts is no longer required.

Source: Hubspot

Sure, you need to bring in new customers; lead generation is, of course, a primary focus for sales and marketing (the funnel). But, for wholesale and distribution businesses especially, repeat orders and returning customers are key to profitability and predictable growth (the flywheel). Customer success is key. Put bluntly, when repeat orders are your bread and butter, you can’t afford to have a leaky bucket!

So, successful wholesale and distribution businesses adopt a growth playbook that combines the funnel and the flywheel to maximize sales and customer engagement.

Source: ProspectSoft

In any business, you want to encourage the flow of new customers coming in, which means attracting the right kind of quality leads that fit your ideal customer profile (ICP). Then, you need to nurture your leads through the sale and increase your close rate.

Doing these three things well will generate more new customers for your business – which is great! But, in wholesale and distribution, even more than other types of B2B sales, it’s critical to successfully onboard customers, increase their average order value and average order frequency, and retain them for longer to maximize customer lifetime value ( CLTV). In other words, shift your focus to existing customers to boost your profits.  

Let’s take a few simple examples that we can all recognize. Distributing coffee beans to coffee shops, bikes to bike shops, packaging to takeaways, or food to delicatessens and restaurants. Whatever you’re selling, the first sale to a customer is rarely profitable on its own. The profit is in the long-term relationship and the repeated supply of goods over time.

So, to create predictable and repeatable growth, we need to get the first three or four orders from the customer so they see you as their go-to supplier. In other words, onboard this customer. Then ensure that we retain them for the long term, upsell them, and reactivate them if they start declining or churning as customers. But what’s all this got to do with RFM analysis? 

If you want to successfully grow and be profitable, you need to know precisely which customers are where in your funnel and your flywheel, how to target each customer appropriately, and what to say to them at each stage in the process – fast and at scale. So whether you’re talking about mass marketing or account management, RFM analysis will allow you to successfully target the right customer, at the right time, with the right message.

Source: ProspectSoft

Here, you can see a range of values for recency, frequency, and monetary value. As an example, using the chart above we could allocate our customers’ recency score. A customer who ordered 18 months ago gets a score of “1”, but a customer who ordered two weeks ago a score of “5”. Obviously, you could label these “buckets” to be more relevant to your average number of orders, how often customers would buy over time, and how much they’d typically spend.

Example

Example Company Ltd’s scores are:

• Recency: 3
• Frequency: 4
• Monetary: 5

For this business, Example Company Ltd is in the top 20% of highest spenders in the last two years and the top 40% of most frequent spenders. Even though their last purchase was nine months ago, they’ve ordered more recently than 40% of the rest of the dataset they’re compared against, making them a “Loyal Customer”. 

Cohorts

If you have very different and diverse sets of customers, you’d want to split these “cohorts” out. An example of the need for cohorts would be if 50% of your customers place really big orders infrequently, and the other 50% place smaller orders often. This is particularly important when it comes to monetary value.

For example, if you have some direct customers who are independent retailers and a supermarket as a customer, there’s no point in comparing all those retailers against the supermarket in terms of monetary spend if there’s a huge disparity there. Or, say you sell to pubs directly, but you also sell to a distributor who distributes in bulk to hotels. The profile of sales to that distributor may be very different from the independent pubs who you’re supplying on a weekly basis.

Here are a few typical anomaly customer examples you might want to exclude from your overall analysis:

• Accounts that aren’t real customers and represent your own Direct-to-Consumer activity. Example: your own Amazon, eBay, or Shopify store
• Accounts that are only there to account for and represent your own ePoS system, like your owned retail outlets or trade counters
• Accounts where you have no control over their spend. Example: large, and genuinely anomalous, accounts that only ever place one large order with you once a year
• Accounts where there’s a reason why they’re hibernating. Example: their business is seasonal, so they temporarily shut down at various points in the year
• Accounts whose spend is very different from the majority of your customers

As a rule of thumb, we’d suggest sticking to two or three cohorts maximum. Remember RFM analysis is about measuring different customers against each other, so you only want to use cohorts if you have very different types of customers.

Build a model using scoring

So once we have these comparative scores, what do we do now? First, apply those scores to each of your customers, and then effectively build a 3D model of your customer’s behavior, similar to a Rubik’s cube.

Unsurprisingly, your best customers end up with a 333 score and your worst customers end up with a 111 score. Effectively, you’re building this out of those three dimensions to put your top customers in the top far corner and your worst customers in the bottom near corner. 

Source: Samba.ai

If you’re scoring 1-5, you get a more complex model (like a 5x5x5 Rubik’s cube). Although this is a more compounded illustration, a representation like this using 1-5 scoring is the classic way to do this calculation, and it allows you to build important profiles of customers.

Source: Samba.ai

Making sense of the analysis

The problem is that it’s quite hard to work with 3D models and even harder to illustrate and visualize them, especially if you want to see all the sides of a 3D diagram at once. So, RFM analysis is usually visualized in a flatter, 2-dimensional pictogram, making it much easier to understand. 

Source: ProspectSoft

In a 2D visualization like the one above, new customers arrive at the bottom right as “New Customers”, and if they begin to spend frequently with a decent value they rise quickly up to “Potential Loyalist”, “Loyalist”, and eventually become “Champions”. But on the other hand, a “Loyal Customer” can drift into “Needs Attention” then “At Risk’”if they begin to spend less often or haven’t ordered in a while.

Ultimately, a customer that doesn’t re-engage fully or cannot be re-engaged then becomes a “Hibernating” customer, and eventually drops out of the diagram as lost or churned in the bottom left. 

The aim is to identify this trend early and act to reverse it long before you get to that stage. To do this, you should be calculating, recalculating, and resharing your values with your team at least once a month, but ideally weekly or even daily, so it’s fluid. Recalculating those “buckets” is also important to do monthly, in case some customers start purchasing more frequently.

All that math is a bit complex and can be daunting, but it is possible to do it manually on a spreadsheet. However, you ideally want a tool, like sales and ops planning software, to automate this and do the heavy lifting for you. Ultimately, for RFM analysis to be effective and accurate, it needs to be calculated every day as your customer and sales data changes.

Why pay attention to lower scores?

Once you’ve got your calculations up and running, it becomes immediately obvious to most managers and business owners that RFM analysis helps you identify the best and most promising customers.

But in a small product business, you often already know who your best customers are. If you spoke to your sales, account management, or customer service teams, you could probably find out who your top-scoring customers are and are likely already building a good relationship with them. So it’s the middle and low scorers that are key to focus on.

The lower scores help you identify areas for improvement. For example, it can reveal things like:

• Your “New Customers” need nurturing and onboarding
• Previously “Loyal Customers” may have gone to a competitor and should be reactivated through a marketing campaign
• Customers who purchase low-value products regularly are prime candidates to be moved up the value chain with an upsell campaign

All of these are opportunities to upsell, retain or reactivate a specific RFM segment which contributes to increasing your CLTV.

• New Customers: If we take our new customers, we can focus on those that have just started to buy from us and onboard them with some personal content that focuses on getting the second, third, and fourth order from them as soon as possible to turn them from transactional buyers into relational customers. 
• Champions: Our Champions can help us grow. We’d want to promote new products, get their feedback on the products we’re selling, and use them to get referrals, good case studies, and perhaps most important of all, use them to define a strong ICP. 
• Hibernating: And, at the bottom end of our customer base are the Hibernating customers. Targeting them with a 20% off offer for any order this month, for example, could be a great way to re-engage them. And of course, we don’t want to send this offer to our entire customer base because our Champions and Loyal Customers will be ordering from us anyway; we’d just be giving away margin unnecessarily.

RFM insights

Looking in more detail at each of the segments, there’s a clear description of each and what makes a customer fit into that segment, as well as actionable insights that you can use for every segment in the RFM analysis.

Source: ProspectSoft

1. Evaluate: Evaluate your Champions and Loyal Customers vs. your Hibernating and At Risk customers to refine your ICP.
2. Compare: Compare your new business campaigns and current leads with your ICP to focus on creating future Champions rather than future Hibernating customers. Use case studies, testimonials, and personal references from existing Champions to close the right leads and secure new accounts. 
3. Onboard: Stay on top of New Customers and Potential Loyalists by fully onboarding them and turning them into regular buyers. If you’ve got lots of new customers coming in, focus on the ones that match your ICP because these are your future Champions. 
4. Develop: Target your existing customer campaigns and special offers to keep pushing new customers up, and older customers to the right, building more high-value accounts. 
5. Upsell: Then, carefully target upsell to those customers that will respond and don’t waste your bandwidth on those that won’t.
6. Focus: Prioritize your account management activity on At Risk and Don’t Lose Them segments, or better yet, use artificial intelligence to predict potential churn alerts to help you focus on these customers quickly and early. 

In summary, RFM is the only analysis for product sellers that creates predictable and repeatable growth every single time!

Now that you’ve completed your RFM analysis and segmented your customers, learn how you can align your sales and marketing teams to hit goals.