RFM analysis: what it is and how to use it

What is rfm analysis?

RFM analysis — is a method of evaluating the user base that helps to understand how valuable customers are to a business. It is based on three key indicators:

• Recency – when a visitor last made a purchase.
• Purchase frequency (Frequency) – how often purchases are made.
• Revenue analysis (Monetary) – how much money a user spent over a certain period.

Thanks to this approach, a business can effectively adjust marketing strategies and focus efforts on the most profitable users.

Why do you need RFM analysis?

The main goal of RFM analysis is to help businesses allocate resources correctly. It allows you to:

• Identify the most valuable users and retain them.
• Identify those who were previously active but have stopped buying.
• Optimize marketing campaigns for different customer segments.
• Improve customer loyalty and increase their level of activity.
   

Business use

RFM analysis is most commonly used in the B2C sector, especially in e-commerce, retail, and service companies. It is used to:

• Improving marketing strategy.
• Adjusting advertising campaigns.
• Identifying target segments for email marketing and other communications.
   

How RFM analysis works

Data collection – is the first and fundamental stage of RFM analysis, which determines the further accuracy of customer assessment and segmentation. At this stage, it is important to obtain structured, reliable information about user behavior, their transactions and interaction with the company.

To perform RFM analysis, you need to collect the following key data:

• Unique customer identifier – it can be a name, phone number, email, ID in the database or an account in the system. It is necessary to uniquely identify the buyer.
• Date of the last transaction (Recency) – This indicator helps to assess how recently a customer interacted with the company, making a purchase or other valuable action.
• Purchase frequency (Frequency) – determines how often a customer makes purchases during a given period. This is one of the most important loyalty indicators.
• Total spending (Monetary) – reflects the financial importance of the customer to the company, taking into account all his purchases for a certain period.

To collect this data, you can use various systems and platforms:

• CRM systems – contain the history of customer interactions.
• ERP systems – are used to track financial transactions.
• Analytics platforms (Google Analytics, Power BI) – collect data about user behavior on the site.
• E-commerce systems (Shopify, WooCommerce, Magento) – store information about orders and purchases.

The period for which data is collected depends on the specifics of the business. In retail or the service sector, data for the last 6-12 months is usually analyzed, while in the B2B sector this period can be longer – up to 1-2 years.

Once all the data is collected, customer segmentation and marketing personalization become possible. Users can be divided into several main groups:

• Loyal – regular buyers with high spending.
• Prospective – those who recently made their first purchase.
• Sleeping – profiles that have not interacted with the brand for a long time.
• Lost audience – those who used to actively buy, but are now inactive.

Different customer interaction strategies are used for each segment, which increases the effectiveness of marketing campaigns and helps optimize sales.

Customer interaction strategies

1. For loyal – reward programs and exclusive offers.
2. For prospective buyers – newsletters and promotional offers.
3. For dormant – reminders and personalized discounts.
4. For the lost audience – special discounts and new offers.
    

Advantages and disadvantages of rfm analysis

Advantages:

• Clear understanding of the base.
• Effective marketing strategy, which is created on the basis of received and systematized data.
• Increase loyalty through personalized approaches.
• Analysis of customer behavior allows for more efficient distribution of advertising budget.

Disadvantages:

• Requires high-quality data for correct analysis.
• Does not take into account behavioral analysis beyond financial indicators.
   

Conclusions

RFM analysis is a powerful tool that helps companies optimize user interactions, increase the effectiveness of marketing campaigns, and improve marketing tools. It allows you to not only understand which profiles bring the most profit, but also build an effective marketing strategy to retain these customers. Using this method helps optimize sales and improve overall business efficiency.