RFM-analysis is essentially one step of communicational personalization between brands and their clients. It allows you to divide a customer base into segments depending on customer activity and give discounts to those who will be interested.
Newsletters
March 9, 2022
What is RFM Analysis?
RFM analysis is a way to segment customers based on order data. It takes into account three criteria: Recency — the date of the last order, Frequency — the number of customer orders for all time, and Monetary — the sum of the cost of all orders. The purpose of the analysis is to build relevant communication means for each of the segments.
In this article, we’ll tell you how marketers increase business revenue through RFM segmentation.
Content:
Why RFM analysis is needed and how it works
Why RFM analysis is needed and how it works
It’s essentially one point of personalization in communication. RFM analysis allows you to divide a customer base into segments depending on customer activity.
You should build your own communication lines with each group of customers. For example, for newcomers with one purchase, propose an offer for a second purchase and recommend related products. For loyal customers, provide VIP discounts and personal offers. This approach to customer communication will allow you to take the customer’s behavioral history into account and save on discounts by offering them only to customers who need them.
How to initiate customer segmentation
Create a single repository for all orders
To divide customers by frequency, amount, and recency of orders, download the entire history of orders into one repository. This part is usually the hardest, because offline sales are often kept in one repository, online sales in another, and call center sales in a third. To build a reliable and valid RFM report, you need a database that will allow you to store sales from all sources in a single repository.
How to segment customers
There are several ways to divide customers. For example, split the base so that each group has an equal number of customers. This method is easy to automate, but “special” segments of customers can be lost. For example, the same group can include those who have made orders worth both $15 and $200. So, another option is to split them according to value ranges. This approach is the easiest to work with. Have a look at the following example:
We split customers into three groups according to their spending patterns: up to $65, from $65 to $135, and from $135. We also created three groups according to their recency of purchase: up to 80 days, 80 to 160 days, above 160 days. In total, we end up with nine segments. In a nutshell, we divided customers by RM (Recency and Monetary) without taking into account F, the frequency of orders.
This example shows division into equal parts by value ranges
These newly-created segments were grouped according to semantic segmentation: “newcomers,” “active,” and “churn.” They may be different for your business, but the point is the same: communicate differently with new, current, and inactive customers.
An example of semantic segmentation is based on the number of customer orders and the recency of the last purchase.
Using segmentation for personalized communication
By dividing your customer base into segments, you can customize triggers for each segment. Below you can see conditional segments created with RFM, to include examples of actual campaigns used for each.
New customers can be motivated to make their first purchase. For example, if a customer leaves their email address but does not make a purchase for several days, an email with discounted products and a personal promo code with a discount on their first order is generated and sent to the customer. This is how it’s done at the MOON-Trade furniture store:
Excerpt from the “Newcomer Reactivation” email
For active customers with purchases, use motivators to initiate repeat orders. The online clothing store 21Shop put together a whole email chain with product recommendations, promotional codes and reminders.
Email with recommendations for the next purchase
An email with a promo code for the next purchase
Promo code reminder email
For example, the SPORTFOOD chain of sports nutrition stores automatically notifies customers if they have bonus points that are about to expire.
Automatic notification of expiring bonus points for an inactive customer
RFM report in CDP Mindbox
The Mindbox CDP platform has an automatic RFM report that divides customers into equal parts by value ranges. You don’t need to configure it, simply download the order data. The report answers the following questions:
- How many customers have never made a purchase?
- How many are loyal customers?
- How many are churn customers?
Information about brand-loyal customers
Checklist: Article Highlights
- RFM segmentation is a way to segment customers according to three criteria: the recency of their last order, their number of orders, and the total value of all orders.
- To build such a report, data on all customers’ orders must be stored in one repository, whether it is an Excel spreadsheet or a CDP platform. The easiest way to build a report is to distribute range values equally and then combine them into semantic segments according to your goals: newcomers, active, churn, etc.
- Fortunately, CDP Mindbox has a built-in RFM, so all you have to do is upload customer data and their orders.
- You can use RFM segments to build personalized communications. Some examples are to offer newcomers a promo code towards their first purchase, send active customers an email with recommendations on their next purchases, or send notifications about expiring bonuses to your churn segment.
The following case study is from Mindbox, the original brand behind Maestra’s technology