How to Use RFM Segmentation to Reach Your Ideal Audience

RFM Segmentation eCommerce Customers
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Stop for a moment and think of the stores you love. It may be your local bakery shop, or perhaps the coffee shop you frequent on your way to work. Perhaps it is a fashion retailer.

Whatever it is, stop and think for a moment of the things that make you go back to that particular store. Now, try to apply the same principles to your eCommerce shop.

Sure, you might not be able to make it smell like fresh coffee in the morning. You might not be able to show your genuine smile every time someone clicks on the Checkout button. However, you can (and definitely should) do everything in your power to increase your relevance towards your customers.

Studies are very clear when it comes to this topic: 80% of your future revenue will come from no less than 20% of your existing customers.

What does RFM have to do with all this? And what is RFM anyway?

Read on to find out more.

Traffic Acquisition vs. Conversion Rate vs. Retention Rate

At the very core, eCommerce businesses aren’t that different from traditional commerce.Surely, the channel differs, and you have a lot more tools to help you analyze your customers.

At the end of the day, it’s all about a product-value exchange. You give customers your products and an extraordinary experience. They give you something in return (monetary value, that is).

With everyone from a 4-year toddler to an 80-year grandma talking about SEO glowingly, some people might feel tempted to believe that SEO is all there is to digital marketing and acquiring customers.

SEO is amazing, don’t get me wrong. Paid Advertising can help as well.

But traffic won’t pay your inventory. Visits won’t pay your site hosting. And pageviews won’t pay the people who work alongside you.

Once traffic arrives, it’s up to you to convert as many of those users as possible.

Most importantly, you absolutely need to retain those buyers as well. In my experience, a loyal customer is hundreds of times more valuable than a newly acquired one. 

This is not just guesswork. It’s insight we’ve drawn after working with a large number of eCommerce stores.

The RFM Model: What Is It and Where Does It Come From?

RFM analysis is a marketing technique used to determine the value of a customer. It’s not a new technique, mind you.

NGOs have employed RFM for more than three decades in direct mail campaigns, to determine which of their recipients was more likely to donate.

More recently, we have seen digital marketers applying this model to their own efforts and campaigns. RFM helps digital marketers accurately segment customers according to the value they offer.

Without RFM, many ecommerce companies segment their customer database only according to one of these verticals: the amount of money they spend on the site, how recent their last purchase was or how frequently they buy. Alternatively, you can use the RFM model to bring all verticals together and segment your audience according to all of them.

For instance, John may make very expensive purchases every six months (e.g. a new component for his high-end computer), while Jill may make smaller purchases every month (e..g 100 USB sticks for her employees).  The value they bring into an eCommerce business could be similar.

However, if you use just the monetary value or frequency of purchases to place John and Jill into the same customer segment, one of them would end up being treated unfairly as compared to the other.

Treating your loyal customers according to their “true” value is hugely important. When identified and marketed to properly, customers will be even more loyal to your business. Even better, they will be much more likely to give you a positive review or recommend your store to their peers.

RFM isn’t just limited to this scenario. It can help you segment your users and create online surveys specific to each of the customer groups, and to conduct qualitative research as well.

With this ability in hand, you will be able to better understand the motivations behind specific purchases, why some people click the “buy” button and others don’t, and what you can do to improve your experience. These are all steps that will help you convert and retain more visitors to your eCommerce website.

How To Calculate RFM

The RFM model is developed on three dimensions: Recency, Frequency and Monetary values.

RFM will assign a score from 1 to 5 on each of these dimensions for each customer, resulting in a three-digit number/ a cell. This three-digit number, or cell, might be 111 for the customer who bought a low-priced product a very long time ago and never bought again. Or perhaps it might be a 555 score for the customer who bought a high-priced product very recently, and made several other purchases as well.

Taking all the possible combinations in consideration, there should be a total of 125 groups of customers with the same score.

Once you have this calculated, you should implement a system that connects types of customers to the RFM score they have received.

For instance, a new customer would be something like this: R = 2 – 3, F = 1 – 5, M = 1 – 5. A loyal one, however, would be something like this: R = 4 – 5, F = 3 -5, M = 3 – 5.

You can manage this process in the traditional way, using a spreadsheet:

  1. Bring together all the data provided by your customer database;
    1. Recency – is given by the number of days passed since last purchase. The smaller the number, the bigger their score will be. Someone placed an order 30 days ago is more likely to buy again, as compared to someone who placed an order 180 days ago;
    2. Frequency – is given by the total number of orders place by a customer
    3. Monetary value – is given by the total amount of $$ spent by that customer with your store
  2. Next, look at the values for R, F, M from each customer and assign a score from 1-to-5 ( with 1 – lowest, 5 – highest)
  3. This is how we assign scores:
    1. Recency
      1. Last order was within 30 days => R score of 5
      2. 30 – 90 days →  R score of 4
      3. 91 – 180 days → R score of 3
      4. 181 – 365 days → R score of 2
      5. 365+ days → R score of 1
    2. Frequency and Monetary: We recommend using quintiles
      1. Customer in Top 5% Frequency and Monetary score of 5
      2. Customer in Top 20% Frequency and Monetary score of 4
      3. Customer in Top 30% Frequency and Monetary score of 3
      4. Customer in Top 60% Frequency and Monetary score of 2
      5. Rest of customers  Frequency and Monetary score of 1
  1. Once each customer has a score for R, F, M → bring them all together in groups (ex: 111 or 555 or 235)
  2. Groups with similar values can be further on grouped together so that they can be better managed (ex: 555, 554, 545, 445)

It may not be the ideal way to do this but it’s quite accurate and results might surprise you.

If the above seems a bit of a mambo jambo, you may want to know that we developed a way of doing this fully automatically and tailored to your store’s data.

We created an automated RFM dashboard extension for Magento, which you can read more about in this blog post authored by Anca Sandu, our Product Manager.

Our extension segments customers in an eCommerce’s Magento database according to the relationship they have with the store:

  • True Lovers – those who are loyal to the store and bring a lot of value to it;
  • Don Juans – those who are flirting with another store;
  • About to Dump You – those who used to buy from you, but will soon move on if you don’t take action.

… And so on. Of course, the names we have used can be changed according to whatever works best for you.

How RFM Can Help You Reach Your Ideal Audience

You won’t find the answers by using a simple segmentation of your customer database.

Once you have your customer segments, it is time to get out and ask them some questions.

  • Why are they making purchases?
  • What is their motivation (intrinsic or extrinsic)?
  • Why do some of them spend more?
  • Why aren’t some customers making a purchase?
  • What can you do to make their experience better?

Of course, these are just some of the questions you could ask. I recommend you prioritize creating surveys and buyer interviews that are suitable for each particular segment.

For someone new on your site, don’t ask them too many questions. Likewise, don’t spam a loyal customer either. Consider each segment and their behavior in relationship with the store, and then start asking questions!

Once you have run a quantitative and qualitative analysis on your existing customers, you will be able to design marketing campaigns that  suit each of the customers already in your database. As a secondary benefit, you will have the knowledge to craft marketing campaigns that will help you convert and retain a larger part of the incoming traffic (and existing new customers who have only placed a lower-value order).

Your ideal audience is closer than you think – right there, in your database. Pay attention to it and what it wants. This is how you grow a customer-centric eCommerce business!

In a world with more than 1.4 million websites built on eCommerce platforms, you are competing for more or less the same customers as all the other eCommerce businesses.

Sure, overall trust in eCommerce has increased significantly over the last decade. Even with that in mind, you don’t want to pour endless amounts of money into merely acquiring traffic. This will eventually hit you financially.

And even worse, you’ll most likely end up with a lot of visits that never buy anything, and never return.

The RFM Model isn’t the only way to segment your customer database. But it is one of the best ones eCommerce managers can use.

We are fans of RFM because it focuses on keeping customers, rather than continuously attracting new ones. When you start treating your customers according to how loyal they are to your online store, you will eventually yield better retention rates. 

Consequently, you will manage to build a steadier, more generous stream of income for your business.


Feature Image Credit: CC 0; Public Domain. All images sourced from Wikimedia.org.

Disclaimer: The views and opinions stated in this post are that of the author, and Return On Now may or may not agree with any or all of the commentary.

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Valentin Radu is a serial entrepreneur and visionary involved in tech and digital marketing for the past 14 years. He is the founder of Omniconvert, a growth enabler tool for mid-size eCommerce websites looking to become customer-centric. Omniconvert is a conversion rate optimization platform that combines the power of AB testing, web personalization, web surveys using an advanced segmentation engine. Valentin is known for his unyielding positive energy, creativity, and knowledge about marketing and business. He is also an international keynote speaker. Most of the times he talks at conferences about eCommerce, optimization, and growth.

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