E-commerce Marketing: Identify High-Value Customers with the RFM Model

In the world of e-commerce, there are always periods of low and high sales. Apart from major sales events and clearance activities, promotional discounts during specific holidays (such as Mother's Day, Singles' Day, and 12.12) often attract significant revenue. However, to reduce the revenue gap between slow and peak seasons, it is essential to systematically enhance customer loyalty and average order value. This ensures that customers' purchasing intent is not limited to major events, promotions, buy-one-get-one-free deals, or site-wide free shipping offers that typically lower the average order value. How can we strategically increase customer loyalty and average order value? The RFM model is the best application model for this.

The RFM model is composed of three dimensions: Recency (the most recent purchase), Frequency (the purchase frequency), and Monetary (the purchase amount). By cross-referencing these three dimensions, brands can systematically segment members based on 'customer value'. This allows marketing resources to be prioritized for high-potential audiences and facilitates effective communication tailored to the characteristics of different segments, maximizing efficiency.

What is the RFM model? How should brands plan marketing activities based on the RFM model? Let beBit TECH share insights through this article!

What is the RFM Model?

The RFM model is a method used to identify and analyze customer value through transactional data. Brands can leverage the three key dimensions to understand customer activity, engagement, and purchasing power, enabling personalized customer management strategies. This approach strengthens brand loyalty, increases sales, and boosts market share.

• R (Recency): The most recent purchase time. The more recent the purchase, the more active the customer is. 
• F (Frequency): The number of purchases within a specific period. The more frequent the purchases, the more valuable the customer is.
• M (Monetary): The total amount spent within a specific period. The higher the spending, the greater the customer value.

The RFM model assists brands in classifying customers into eight advanced segments based on the three key dimensions (Recency, Frequency, Monetary) to determine customer value and formulate appropriate communication strategies:

• VIP Members (High R, F, M): High purchase frequency, high spending, and recent purchases. These members are valuable assets to the brand, showing a strong preference for products. Focus on exclusive gifts and new product discounts to encourage continued engagement and positive interaction with the brand.
• Attentive Members (High R, Low F, High M): Low purchase frequency, high spending, and recent purchases. Although these members purchase infrequently, their high spending amounts in single transactions can quickly elevate them to VIP status. Offer exclusive, sporadic discounts to remind them of the brand during the repeat purchase period.
• Potential Regular Customers (High R, High F, Low M): High purchase frequency, low spending, and recent purchases. Despite their lower spending amounts per purchase, their high frequency indicates strong loyalty. Use tiered discounts or special bundles to increase their average order value.
• Standard Potential Customers (High R, Low F, Low M): Low purchase frequency, low spending, and recent purchases. These customers may have been drawn in by major promotional events but spend less. Focus on re-engagement strategies during key promotions to encourage more frequent purchases.
• Maintained Customers (Low R, High F, High M): High purchase frequency, high spending, but have not purchased in a while. These are former VIPs and significant revenue contributors who have not purchased recently. Collect feedback and use nurturing scripts to understand and address why they have disengaged from the brand. 
• Sustained Customers (Low R, High F, Low M): High purchase frequency, low spending, but have not purchased in a while. Previous frequent buyers who have not returned for a while. Investigate any major changes in promotional activities that might have caused their disengagement and address these factors to encourage their return.
• Key Retention Customers (Low R, Low F, High M): Low purchase frequency, high spending, but have not purchased in a while. High-spending customers who purchase infrequently. Target them with new product promotions to strengthen brand awareness and confidence.
• Dormant Members (Low R, F, M): Low purchase frequency, low spending, but have not purchased in a while. These members are the lowest priority and may lack trust in the brand. Offer discount codes and concern letters, but if there is no response after a period, consider removing them from the mailing list.

Common Applications of the RFM Model

After segmenting all members based on recency of purchase, purchase frequency, and purchase amount, how do you plan corresponding customer management strategies? How can these strategies effectively drive performance? How do you assess whether current marketing activities can attract high-value customers? Here, we will share applications of the RFM model:

1. Optimizing Membership Programs
2. Optimizing Pricing Strategies

Additionally, we will use examples from the apparel and health supplement industry to illustrate planning strategies for the RFM model segmentation and communication according to the characteristics of each industry. 

Utilizing RFM Data to Establish Membership Programs and Increase Repeat Purchase Rates

‘Membership programs' are the most common segmentation method in e-commerce. Brands usually categorize customers into general members, silver members, gold members, etc., offering exclusive discounts, birthday gifts, and other perks. How can you effectively plan tier differences and upgrade systems? The RFM model can serve as a reference.

Based on the RFM model's F (Frequency) and M (Monetary) dimensions, you can analyze consumers' purchasing history. For example, if the annual spending ranges of 6,000-8,000 and 12,000-15,000 are the highest in terms of revenue contribution, these intervals can serve as the annual spending conditions for membership tiers. Additionally, the distribution of annual purchase frequency can be referenced to set upgrade thresholds. Brands should regularly use the RFM model to review the appropriateness of current membership programs. In terms of tier mobility, automated communication can be implemented with OmniSegment CDP's ‘upgrade reminder scripts,' which can effectively generate an additional 5-8% in monthly revenue contribution.

Optimizing Pricing Strategies with RFM Data to Increase Per Capita Contribution

The RFM model can also help in planning monthly product combinations! Often, we increase the order value (average order value) through celebrity bundles and add-on purchases. When designing these combinations, consider using RFM-segmented audiences for more precise pairing: offer exclusive VIP bundles to high-contribution members, and more attractive promotional bundles to less active members. This approach does not only enhance the willingness to purchase product bundles, but also extends the per capita contribution across different segments.

Industry Application Sharing: Apparel and Health Supplement Industries

Apparel Industry

In the fast-changing environment of the apparel industry, sensitivity to repeat purchase indicators is crucial. According to market observations by the beBit TECH team, members who do not perform repeat purchase within a season are likely to churn rapidly.

Managing customer repeat purchases relies heavily on the R (Recency) indicator in the RFM model. Using the built-in brand RFM analysis module in OmniSegment CDP, automated communications can be triggered at 90 and 180 days since the last purchase. This approach encourages less active but high-value members (such as Maintained Members and Key Retention Customers) to return, boosting monthly revenue by nearly 10%. Since each member's repeat purchase date varies, the system's automated delivery can customize marketing content for different members more effectively.

Health Supplement Industry 

Customer loyalty is often the primary goal in the health supplement industry. Brands hope that customers will perform repeat purchase consistently after using their products and consider buying other types of health supplements to address additional health concerns. In cultivating brand loyalty, prioritize members who perform well in the RFM analysis module (such as VIP Members, Attentive Members, and Potential Regular Customers). Offering exclusive hidden product page promotions through automated delivery scripts can effectively contribute 6-8% of total revenue over five months. By targeting high-value segments first, brands can quickly expand their basic revenue base.

The two examples above share the primary goals of driving more sales and cultivating more loyal users. However, due to the different industry types and the unique appeal of each marketing activity, it is essential to use the RFM model, composed of operational data, for more in-depth observation in marketing strategies and member communication. This will enable more appropriate planning and application of marketing content.

Conclusion

Through the above explanation, brand marketers should now have a clearer understanding of how to categorize customers' value using the RFM model, providing a more precise direction for marketing activity planning and resource allocation. Additionally, OmniSegment CDP, recognized as the top customer data platform for e-commerce and brands, features a built-in RFM analysis module that supports more diverse marketing applications. If you're unsure how to conduct an analysis, or want to learn more about analysis strategies, feel free to contact the experts at beBit TECH, who can provide tailored data analysis recommendations based on your needs. 

🔎 Simply fill out the form on the Contact Us page and mention 'Request RFM Template' to receive a complete RFM data recording template, assisting your team in subsequent data recording and analysis!