RFM analysis

RFM (recency, frequency, monetary value) analysis is a marketing method used to identify the best clients based on their spending habits. RFM helps predict which customers are likely to repurchase a company’s products and estimate its revenue from regular and new consumers.

In this article, we’ll talk about why you should use RFM analysis, explain how it works, and review how to perform this analysis.

Why should you use RFM analysis?

This marketing tool entails three main quantitative categories: how recently a client bought a product, how often this person makes purchases, and how much money this customer spends. It allows companies to rate their customers and identify those who bring the most value. By conducting the analysis, marketers compare the profit that existing and new customers bring to their businesses.

RFM analysis helps companies manage their advertising budget wisely. It enables marketers to identify consumers with the same values and segment them. Audience segmentation allows brands to create targeted campaigns, tailor their messaging, meet customers’ needs, and enhance customer satisfaction and ROI.

The method is critical for marketers since it provides them with an understanding of customer behavior, which influences retention, customer lifetime value, and engagement. After conducting RFM analysis, marketers can identify clients’ level of satisfaction, interest in promotions, and spending volume.

To conduct the analysis and reap the benefits of this technique, you should know what aspects to pay attention to, which is why we need to figure out how RFM analysis works.

How does RFM analysis work?

The first step of RFM analysis is ranking customers according to the following factors:

• Recency. The more recent the purchase, the higher the likelihood of a customer remembering the brand and keeping it in mind for the next transaction. Recent customers are more likely to buy something compared to those who haven’t made any purchases for months. These facts are essential for companies since they help single out more recent customers and encourage them to buy again soon.
• Frequency. Many aspects influence customers’ buying frequency. They include the product type, price point, and restocking necessity. Companies can anticipate the demand, for example, products purchased today will run out, and clients will have to repurchase them soon. Since it’s a repeatable process, brands can predict the date of the next purchase and direct their marketing efforts to remind customers to visit their stores or websites when consumers run out of products. This way, brands can gain customer loyalty because customers love to be taken care of.
• Monetary value. This is the amount of money each customer spends with a brand. Companies encourage their consumers to spend more to reach their revenue goals.

Businesses pay attention to these three factors and rate customers from 1 to 5 (5 is the highest score). Marketers calculate clients’ ratings and identify customers with the highest value (the best consumers). Then, they use this data to create personalized advertising campaigns, offers, or promotions to improve ROI.

Now that you know how RFM analysis works, you should explore the steps required to conduct it. Let’s have a closer look at the process itself.

How to Conduct RFM Analysis

Different tools can help you perform the analysis. For this purpose, you need to have a CRM system with your customer base. Different platforms can import your customers’ data from CRM, calculate the RFM, and provide you with the results.

You can also consider using a spreadsheet in Excel or Google Sheets as your RFM analysis tool. To get started, you need to export every client’s purchase history from your CRM database to the spreadsheet. Next, you need to sort your customers based on the three key factors: recency, frequency, and monetary value. Rate every customer, and give them a score from 1 to 5 (5 being the highest score).

Below you can see an example created with Google Sheets. The table below contains customers’ names, last order date, recency, order frequency, and purchase amount that can help score each client. You can also see the key factors of RFM analysis.

Let’s take one of the customers and rate this person. For example, Richard’s scores are 2,2,3, so his RFM score will be 2.3.

This data-driven method helps companies segment customers based on their similarities and make strategic decisions on the upcoming campaigns and actions directed toward encouraging these clients to perform more transactions. With its help, brands can increase ROI, improve customer satisfaction and retention, and create personalized campaigns.