How to Use RFM Analysis for Better Digital Marketing

RFM Analysis is a customer segmentation technique that helps businesses identify valuable customers based on their spending behavior.

I was first exposed to RFM analysis early in my career in direct marketing. It’s an analysis technique that catalog marketers developed to minimize marketing expenses and maximize the return on ad spent (ROAS). Using customer segmentation allowed catalog marketers to send catalogs to the customers most likely to purchase again. Direct mail marketing further refined and confirmed that RFM segment marketing works because RFM factors can be used for predicting future customer behavior.

This article will explain the RFM analysis method and how marketers can use RFM segmentation to optimize RFM marketing campaigns.

RFM analysis is based on the idea that the customer who bought from you most recently, who buys from you most frequently, and who spends the most money with you is the customer most likely to buy again.

As mentioned above, RFM analysis was first used by direct mail, non-profit, and catalog marketers. RFM data analysis was used to segment customers and target those that are most likely to buy again. This could be a donation or another purchase from the catalog. It could also be a cross-sell or an up-sell. The reasoning behind the practice is simple – people who donated or bought from you in the past are likely to buy or donate again.

What is RFM Analysis?

RFM segmentation uses raw data to identify the best customers by analyzing their purchase behavior. As suggested above, RFM analysis uses three data points:

• Recency: The number of days since a customer’s last purchase.
• Frequency: The number of purchases a customer makes over a specific period.
• Monetary Value: The total amount of money a customer spends over a specific period.

The RFM segmentation model uses these three factors to segment customers into different groups based on their spending behavior. Businesses can then use the RFM segmentation to create RFM marketing campaigns that target high-value customers to improve retention and sales.

RFM analysis is a proven technique used to identify high-value customers that are more likely to convert, which means more effective Digital Marketing. Click To Tweet

Segmenting Customers Using RFM

The first step to implementing the RFM model is to gather customer data. This data can be collected from a customer database that contains information on customer transactions and historical data. It’s especially easy for an e-commerce business to perform RFM analysis, but most businesses can find the data that’s needed.

Once the data has been collected, you can calculate RFM metrics. This involves scoring each customer on a scale of 1 to 5 for each of the RFM metrics – recency, frequency, and monetary value.

The customers are then segmented based on their RFM scores, and each group is assigned a label. For example, customers with a high RFM score are labeled “most valuable customers,” while customers with low scores for all three factors are labeled “other customers.”

How to run an RFM Analysis Using Excel

David Langer published this tutorial on YouTube explaining how to use Excel to perform your RFM analysis. He mentions several times how easy it is. Your mileage may vary.

Using RFM Analysis for Digital Marketing

Businesses can use RFM segmentation data to identify their loyal customers, repeat customers, and new customers. RFM analysis can also predict future behavior. The customer who bought from you most recently, who buys from you most frequently, is the customer who is most likely to buy from you again. Basically, digital marketers can use the RFM segments to target marketing campaigns and tailor messaging and offers to fit their needs.

For example, RFM segments are especially effective for email marketing loyalty programs. Customers with a high RFM score are more likely to generate sales, so increased loyalty is very important. They are heavy users, which means the products and services they purchase from your business are important to them. Good customers expect to be rewarded, so additional information about how they can use their purchases will be seen and valued.

New customers can be targeted with appropriate messaging, but if the RFM model doesn’t award them a high RFM score based on their purchasing behavior, they will stop receiving marketing.

Changes in customer behavior can also be tracked using the RFM model. If a customer drops out of the top quintile, this can trigger an email with an attractive offer. Equally, if a customer joins the top quintile, a special message can be sent to them to enhance their monetary value further.

A digital marketer can also use RFM analysis to identify their best customer segment and then use this file to create look-alike audiences for Social Media Advertising or Google ads. Ads targeting high-value customer segments will perform better.

Customer Segmentation – An RFM Use Case

Suppose a business wants to target its marketing efforts toward its best customers. In that case, they can use RFM analysis to identify who among their existing customers has a high RFM score and create a targeted marketing campaign for this audience.

To do this, the business would use RFM analysis to segment its existing customers based on their RFM score (recency, frequency, and monetary value score) and identify the customers with the highest RFM score.

The business could then create a targeted marketing campaign for these customers, such as a loyalty program. By doing so, they can increase customer loyalty, leading to increased revenue and profitability.

Caveat Emptor

Although RFM calculations and analysis are useful tools, they do have their limitations. For example, a business should avoid over-soliciting customers with the highest rankings. Use the 70:20:10 rule; 70% of your content is designed to create value for customers, 20% is curated content that your customers will value, and 10% is product- or sales-oriented.

Conversely, marketers should remember that consumers with lower RFM scores, while they shouldn’t be ignored, are unlikely to ever be great customers. They simply don’t need your product as much as your best customers. RFM analysis helps identify these customers too. They will be the majority of your customers, but they don’t represent the majority of value, so don’t spend your budget on them. All the marketing in the world is not going to change how much or how often they need your product. Use your RFM digital marketing budget to maximize ROI by focusing on your best customers for greater success.

Conclusion – RFM Analysis and Digital Marketing

RFM analysis is a powerful customer segmentation technique businesses can use to identify valuable customers and create targeted marketing campaigns. By using this method, businesses can segment customers based on their spending behavior and create campaigns that focus on each segment’s specific needs.

To get the most out of RFM analysis, businesses need to ensure they have accurate data and understand how to use it effectively. By doing so, they can improve customer satisfaction, increase customer loyalty, and ultimately boost revenue and profitability.