RFM (recency, frequency, monetary value) analysis is a marketing method used to identify the best clients based on their spending habits. It helps predict which customers are likely to buy products again and estimate revenue from regular and new consumers.
In this article, we’ll unveil the reasons to use RFM analysis and how it works. We’ll also 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 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 an existing and a new customer brings to the business.
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 messaging, and meet customers’ needs. As a result, higher level of 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 the level of satisfaction, interest in promotions, and the volume of spendings.
To conduct the analysis and reap the benefits of this technique, you should know what aspects to pay attention to. This is the reason why we need to figure out how RFM analysis works.
How does RFM analysis work?
RFM analysis starts with ranking customers according to the following key factors.
- Recency. The more recent the purchase was made, the higher is the likelihood of a customer to remember the brand and keep it in mind for the next transaction. Recent customers are more likely to buy something rather than those who haven’t made any transactions for months. These facts are important for companies since they help single out recent customers and encourage them to purchase again soon.
- Frequency. Many aspects influence the frequency of customers buying products. They include the product’s type, the price point, and the necessity for restocking. Companies can anticipate the demand. For example, the products customers buy today will run out in a certain timeframe. After a while, they will go to the grocery store again. 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 the store when consumers run out of products. This way, brands can gain customer loyalty because customers love to be taken care of.
- Monetary value. The factor focuses on 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 3 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 already know the way the analysis works, you should explore the steps needed to conduct RFM analysis. Let’s have a closer look at the process itself.
How to do RFM analysis?
Different tools can help you perform the analysis. For this purpose, you need to have a CRM with your customer base. Different platforms can import your customers’ data from CRM, calculate the RFM, and provide you with the results.
As an option, consider using a spreadsheet in Excel or Google Sheets for the analysis. To get started, you need to export the purchase history of every client from your CRM database into the spreadsheet. The next step is to sort customers based on the three key factors: recency, frequency, and monetary value. Rate every customer and give a score from 1 to 5 (with 5 being the highest score).
Below you can see an example created in Google Sheets. The table below contains the names of customers, their last order date, recency, order frequency, and purchase amount that help score each customer. You can also see key factors of RFM analysis.
Let’s take one of the customers and rate this person. For example, customer number 5 — Richard. The client’s scores are 2,2,3 and the RFM score will be 2.3.
The data-driven method helps companies segment customers based on their similarities and take strategic decisions on the upcoming campaigns and actions directed on encouraging these clients to perform more transactions. With its help, brands can increase ROI, improve customer satisfaction and retention, and create personalized campaigns.
- This article defines the term and provides an example.
- In this article, you’ll find the limitations of RFM analysis.
- This article provides readers with reasons to implement the analysis.
Last Updated: 06.10.2021