RFM Analysis: What is it in Marketing

Definition

RFM (Recency, Frequency, Monetary) analysis is a technique used in marketing to segment customers based on their purchasing behavior. It allows you to determine how recently a customer made a purchase (Recency), how often they make purchases (Frequency), and how much they spend on each purchase (Monetary). RFM analysis helps identify the most valuable customers, as well as develop personalized marketing strategies to retain and attract new customers.

Recency reflects how recently a customer made their last purchase. The closer the purchase date is to the current moment, the higher the Recency value. The goal is to identify customers who have recently made a purchase, as they tend to be the most active and loyal to the brand.

Frequency indicates how often a customer makes purchases. The more purchases a customer makes, the higher the Frequency value. Customers with a high purchase frequency can be valuable because they are likely to be loyal and regular buyers.

Monetary (amount) reflects the average amount a customer spends on each purchase. The higher the amount, the higher the Monetary value. Customers who spend more money on each purchase can be the most profitable for a business.

RFM Analysis: Definition and Application in Marketing

To conduct RFM analysis, it is necessary to collect and structure customer data, including information on the date of the last purchase, the number of purchases in a certain period, and the total amount of expenses. Then, each customer is assigned a score for each of the three parameters according to their values. For example, a customer who made a purchase recently is assigned a high score for Recency.

After this, customers are segmented based on RFM analysis. Typically, quartiles are used to divide customers into groups. For example, customers with high Recency, Frequency, and Monetary values are placed in the “Best Customers” group, while customers with low values are placed in the “Lost Customers” group.

 Template for effective customer analysis

The RFM analysis template in Excel consists of several sheets and columns, each of which is responsible for one of the RFM parameters – Recency, Frequency, Monetary. The first column of each sheet indicates the client identifiers or their unique names.

The Recency sheet shows the date of each customer's last purchase. For convenience, you can use the date format to easily determine how long ago the purchase was made. In the column next to each date, you can specify a formula that will automatically calculate the number of days that have passed since the purchase date until the current moment.

The Frequency sheet shows the number of purchases made by each customer over a given period. Here you can simply enter the number of purchases in the appropriate column.

The Monetary sheet shows the amount spent by each customer on purchases. Here you can specify the total amount spent or the average amount per purchase

To conduct RFM analysis, you can use an Excel template. Here is an example of a template that can be used to analyze customer data:

In this example, we have information about five customers and their purchasing behavior. For each customer, we specify the Recency, Frequency, and Monetary values.

After filling in the data on each sheet, you can begin to conduct the analysis. To do this, you can add formulas to each sheet that will calculate points for each RFM parameter. For example, for Recency, you can use a formula that will assign a high score if the purchase date is closer to the current moment, and a low score if the purchase was made a long time ago.

How to segment customers

Once you have calculated the scores for each parameter, you can begin segmenting your customers. You can use conditional formatting or filters to divide customers into groups based on their Recency, Frequency, and Monetary scores.

RFM analysis template in Excel allows companies to easily structure customer data and gain valuable insights to develop personalized marketing strategies. It helps identify the most valuable customers who generate more revenue and make purchases more often. This allows companies to focus their efforts and resources on retaining and developing these customers, as well as attracting new customers with a similar profile.

RFM Analysis Template in Excel is an effective tool for analyzing customers and developing personalized

Once the data is filled in, you can begin the analysis. To do this, you can assign points to each of the three indicators. For example, you can use a scale from 1 to 5, where 5 is the highest value. You can then calculate the RFM sum by adding up the values of the three indicators for each client.

Customers can then be divided into segments using quartiles or other data partitioning methods.

For example, you can divide clients into three groups:

Once customers are segmented, the company can develop personalized marketing strategies for each group. For example, for “Best Customers,” you can offer exclusive offers, bonuses, or discounts to retain them and encourage them to make further purchases. For “Lost Customers,” you can develop a recovery program, such as sending them personalized offers or invitations. For customers in the low RFM segment, you can develop marketing activities to engage them and attract them to the brand.

The ranges for each indicator can vary significantly depending on the type of company. For example, if your business is related to the sale of household appliances or furniture, then you can consider the rating “good” if the customer makes a repeat purchase in 6 months. However, if your company is engaged in food delivery, then you can consider the rating “bad” if the customer does not make repeat orders within 2 weeks. It is also important to consider that purchase amounts will vary depending on the type of business. For example, for a furniture store, the amount of 10,000 rubles may be considered low, while for a food delivery company, this may be a high check.

The formation of segments also depends on the specific conditions and goals of the company. As a result, you can create 27 different segments (333) with ratings such as 111, 112, 113, 121, 131 and so on to more accurately analyze and engage with your audience.

RFM analysis is a powerful tool for customer segmentation and developing personalized marketing strategies. It allows companies to use their resources more efficiently, improve customer engagement, and increase sales. Using an Excel template and analyzing Recency, Frequency, and Monetary data, you can identify your most valuable customers and take steps to retain and attract new customers. With RFM analysis, companies can more accurately define their target audience, offer more relevant offers, and improve the overall customer experience.

Sometimes, to create unique segments, it is enough to consider just two key indicators.

RF Analysis: By Recency and Frequency: This analysis allows you to determine how often your customers make purchases in a given time period. This is a great way to identify customers who have recently purchased your products and are repeat customers.

RM Analysis: Distribution of clients by the parameters “Recency” and “Monetization”. This type of analysis helps to identify the clients that bring the greatest profit, as well as those who make an insignificant contribution to the overall income.

FM Analysis: Distribution of customers depending on the “Frequency” and “Amount” of purchases. This method identifies customers who make small purchases with high amounts, as well as those who make frequent purchases but with small receipts.

Based on the data you receive, you will be able to develop a strategy and tactics for interacting with each segment of your customer base.

For example, in the “333” segment there will be customers who have not bought for a long time, made only one small purchase. At first glance, they may seem less promising, but you should not abandon them. After all, they have at least once shown interest in your products.

On the other hand, the “111” segment is the “champions” of your customer base. They buy frequently, make large purchases, and have recently made a purchase. These are the customers you should treat with great care.

For each segment, you can develop customized offers with special conditions and start building communication. You can adapt communication for each segment or limit it to a few priority ones, depending on your goals.

How to Use RFM Methodology Effectively

RFM analysis in marketing is a tool with a long history, which has been helping to optimize the return on investment (ROI) of advertising campaigns for several decades. Typically, the application of the RFM method in advertising consists of creating unique creatives and texts for each audience segment. This approach is also used when it is necessary to classify clients by their activity and characteristics.

Customer segmentation is something quite standard in the world of marketing. Large companies strive for maximum detail in customer segmentation, and experts engaged in this task develop clearly structured customer segmentation strategies.

“RFM analysis can be your faithful companion in increasing the lifetime value of customers (LTV). Much depends on how much your customers are willing to spend during the entire period of interaction with your brand, and here RFM analysis comes to the rescue. With its help, you can reduce customer losses, promote additional products and services to the best segments, strengthen loyalty and arrange word of mouth, sell more expensive products and services, and much more.

However, you should remember about moderation. If you address the same segment of customers too often, you can cause irritation and loss of customers.

Pros and cons

The success of using RFM analysis in marketing is confirmed by a huge number of successful cases provided by retailers, entrepreneurs in the HoReCa, beauty industry and other professionals in the field of entrepreneurship.

Advantages of RFM Analysis

Limitations of RFM Analysis

RFM analysis is undoubtedly a powerful and important tool for marketers and entrepreneurs who want to better understand their customers and optimize their marketing strategies. This method allows you to classify customers based on their activity and purchasing behavior, which in turn facilitates more accurate and personalized communication.

The benefits of RFM analysis are clear: the ability to create highly targeted advertising campaigns, improve customer experience, optimize marketing costs, and increase customer loyalty. It is important to remember that RFM analysis is just one tool in a marketer’s arsenal, and its successful application requires careful analysis and integration with other marketing strategies.

However, as with any tool, RFM analysis has its limitations, including limited applicability in some scenarios. It also requires access to customer data and careful analysis. However, when used correctly, RFM analysis can significantly improve marketing efforts and increase campaign effectiveness.

In conclusion, RFM analysis remains an important tool for marketers and business leaders to better understand and consciously engage with customers. When successfully applied, it can help improve results and customer satisfaction, which can ultimately lead to increased profits and business success.