RFM Segmentation

RFM segmentation: A data-driven strategy for every customer

How do you segment your customer bases? Are you adapting your strategy to the multiple variables that characterize your audience? What do you use to track clients’ evolution and to respond to their actions?

Whether you’re looking to target a wider audience, narrow your messaging to promote a new product, or to influence your brand impact stats, a Recency, Frequency, and Monetary value analysis, or RFM, is where you need to start. The benefits of carrying out this exercise are:

  • Higher return on investment in marketing initiatives
  • Greater operational efficiencies
  • Better understanding of your audience, to improve their customer experience (CX)
  • Boost recurrent purchases and value of spending per customer
You can use RFM segmentation to develop initiatives that attract your perfect customer base, while you focus on the best value-for-money audience segment and delivering a world-class CX. But first, you need to know how to carry out the analysis.

How to analyze RFM for your ecommerce

There are several ways to analyze and classify your customers. RFM segmentation consists of grouping them into segments, based on three variables, from whose initials the analysis name comes:

  1. Recency: How recently, time-wise a customer made their latest purchase.
  2. Frequency: The frequency with which they have made purchases.
  3. Monetary value: The amount spent at your business through their customer lifetime.

As an RFM analysis example, consider a fashion ecommerce selling luxury clothing across North America. To use the monetary value variable as an example, let’s assume all of the sellers’ customers ordered different values of clothing, either by order size or garment price. As part of an RFM analysis, the fashion brand would rank these customers from lowest to highest expense and divide them into five groups of equal proportion. Customers with the lowest total amount will be in group M1, while those who have spent the most will be in group M5. 

RFM Analysis example

The same calculation should then be carried out for the variables of recency and frequency. Consequently, an RFM value can be obtained, consisting of three numbers. This calculation varies greatly by business, location, audience, and niche. 

Returning to the fashion brand example: customers who bought a garment yesterday, having bought more than one garment that week, and who purchased a large monetary amount of clothing, might have a value of R5-F5-M5 — the most valuable for this business.

RFM Analysis in Excel

You can carry out an RFM analysis in Excel, or any spreadsheet tool. It can be done manually or you can build formulas to simplify the process since you will need to repeat it every month or quarter, ideally.

Scores assigned, audience segmented. Now what’s the plan?

Any business completing this analysis will discover a high number of combinations of the three variables, 125 combinations to be exact. To make this manageable for business owners, Aument can help you group them into segments. As a result, you can identify the amount of time and energy that each audience segment merits, their needs and preferences, and implement actions that are relevant for each of their CX.

By developing an RFM analysis in Excel or your preferred tool, you will be able to recognize your group of high potential clients — those who have made a single transaction recently, of high monetary value — as well as those who register transactions with a high frequency and for a high value, but a long time has passed since their last purchase. 

Each segment is worth spending time nurturing, be it through personalized messaging, automated reminders, or special discounts. The key is to know which segments need what sort of communication, offers, and prompts. This way, you can remain relevant to each one, and maximize purchases.

How to use RFM segmentation to inform business decisions

As well as marketing spend, the other business area that an RFM analysis might inform is your operations growth.

In the case of the fashion business, the owner could use their RFM segmentation results to decide whether it’s worth setting up distribution offices or contracting manufacturing partners in neighbouring countries, in order to ship faster to international repeat customers. In turn, this might help the owner to save on costs and boost the monetary value of each loyal customers’ lifetime.

For the purpose of this exercise, consider a repeat customer to be the same as a frequent customer.

How are the big brands analyzing RFM?

RFM analysis differs between businesses. A clothing accessories store is not going to detect the same customer frequency or total amount of purchase as a health food store. You will want to focus on attracting a larger valuable group for your business each quarter, whether that means targeting R3-F3-M3 and upwards, or only nurturing R5-F5-M4 customers. Find your balance, before competing with other companies.

The traditional RFM approach is to segment audiences into 11 groups, but Aument designed an improved customer segmentation method, to help you dedicate your time more efficiently:

The 5 Aument segments

In 2021, we coined an original strategy in order to simplify your client retention.  Inspired by The Case of Online Stores in Morocco, our method of segmenting clients, based on data, is ideal for busy online shop owners. It is more realistic to manage the 5 Aument segments, than the traditional 11.

We use historical data to inform your segmentation. Our analyses have shown that certain customer groups can behave similarly in terms of propensity to buy, even if they belong to separate clusters. The percentage of your audience in each group varies widely by company stage and industry. 

Here are the 5 Aument segments that we generate to help your company refine its targeting:

  • Champions

This top segment is made up of your longest-standing customers, who usually make the most expensive purchases. Champions buy frequently and repeatedly.

  • Loyalists

In general, loyalists are your group of customers who buy very frequently, but spend less than champions. Their customer lifetime length is not a key factor for classification.

  • Customers at risk 

Your largest group will likely be customers you could classify as at risk. These people have neither shopped frequently nor recently and may have also spent very little during this visit.

  • Lost customers

If customers stopped buying long ago, below your recency average, but had been purchasing from your brand for a long time prior to dropping off the radar, they are considered lost.

  • New customers

This group has a lot of potential, and encompasses all customers who were acquired very recently, for the first time. In general, these customers make purchases of a lower value than the average customer buys in your shop.

RFM segments

If you came here looking for an RFM analysis example, we hope this has helped. Keep in mind that Aument’s platform not only offers analysis, segmentation, and strategies that targets each of these segments, it suggests personalized automations and preselects audiences plus channels through which you can build retention and attract more clients, all at the touch of a button. 

Ready to hand over the workload, and tailor your communications and customer nurture efforts? Tap into our data-driven AI technology today.

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