What Is Recency, Frequency, and Monetary Value (RFM)?
Recency, Frequency, Monetary Value (RFM) Model is a marketing technique that analyzes consumer behavior and various purchase patterns and segments customers into groups based on these patterns. This model can be used to find the best customers for the company. RFM is a data-driven approach and it takes into account 3 main factors of customer behavior.
- Recency: How recently has the customer made a purchase from the company
- Frequency: How frequently does the customer make a purchase from the company?
- Monetary value: How much money does the customer spend on each purchase?
Each of these factors are then given scores. These scores are combined to chalk out an overall RFM score for every customer that the company has. This score can then segment the customers based on their behavior and buying patterns. This allows businesses to focus their marketing on their key customers. This can help streamline sales as well as customer service.
There are various applications of the RFM model. You could offer exclusive discounts or rewards to those existing customers, who have good loyalty and are spending high. On the other hand, you can promote products and push repeated purchases to those customers who have lower loyalty and spending.
By using RFM analysis, businesses can optimize their marketing, increase sales and revenue, while simultaneously increasing customer retention and employee satisfaction. Let us understand the 3 main factors of the RFM Model
Recency is the amount of time that has elapsed since the customers last purchase from the business. We need to measure recency because it tells us how engaged the customer is with the brand. If the customer is more engaged, they are likely to make further purchases with the brand.
In essence, the more customers you have who have made recent purchases, the more loyal customers your company has which is a very good metric for your business.
Company’s can look into the customer database and calculate the recency score by looking at the number of days or weeks that have passed since customer interaction. Those who have purchased recently will have higher recency score and those who have not made purchases recently will have a lower recency score.
- Company can use personalized promotions or discounts to keep customers with high recency score engaged with the company.
- Customers with low recency scores can be sent general marketing messages to initiate brand recall and repeat purchases.
Frequency is the number of times a customer has repeatedly purchased or engaged with your brand in a given period of time. For example: If customer A has bought 10 times in the last 1 month from your brand, and customer B has bought 5 times, then customer A is a more frequent customer.
Frequency measurement is important in RFM model because it indicates how loyal or engaged the whole customer base is with the brand. You can take any length of period and find out your most frequent customers and then focus on those customers who are less or more frequent and increase marketing to them accordingly.
To find out frequency, extract a data of customers who have purchased products in the last 6 months (Or more) and find out those customers who have purchased most frequently. Customers who have made more repeated purchases have high-frequency score, and customers who have made less repeated purchases have low-frequency score.
The frequency score segmentation can give valuable insights to help businesses tailor the marketing and customer service accordingly. Similar to recency, you can offer discounts and loyalty programs to customers who are more frequent to keep them engaged. Whereas you can send reminders and make marketing efforts to engage with those who are less frequent.
Monetary value is the amount of money that a customer spends on your brand. If a customer spends more money he is known to have more monetary value, whereas if a customer spends less money, he is known to have less monetary value.
For example- Decathlon is a sports goods company. If someone is regularly hiking or playing sports, they will have a high monetary value to Decathlon because they come once and buy larger number of products. On the other hand, if someone is unfit and doesnt do much exercises, they have lower monetary value for Decathlon. So Decathlon as a company will focus its marketing and customer service efforts towards the higher monetary value buyer.
To find out the customers with most monetary value, extract data of a specified amount of time and find out the customers who have the most spends. If you take historical data and link it with frequency, you have invaluable data of who is your key customer and who needs to be focused on.
Similar to other data analysis,, you can focus on customers who spend a large amount on your brand. However, monetary value is not always an indication of a customer who will keep spending. Thus, you should not alienate the frequent customers by focusing exclusively on monetary spenders.
Importance of RFM Analysis
- It utilizes numerical scales that are objective in nature and yields a precise and high-level informative depiction of the customers of the firm.
- It is quite simplistic in its nature and approach and the marketers can use it effectively without the requirement of the data scientists or any sophisticated software.
- It is intuitive as the output of this segmentation method is easy to understand and interpret by the management of the firm.
- Recency can be explained as how much time has been elapsed since a customer’s last activity or the purchase indulgence with the brand? Activity is usually a purchase, although there are certain variations such as the last visit to a company official website or use of a mobile app. In most cases, the more customer has interacted or transacted with a brand, the customer will be more likely responsive to communications from the brand.
- Frequency can be explained as to how often a customer transacted or interacted with the brand during a specific period of time? Customers with frequent activities are more engaged and involved and probably more loyal to the brand as compared to the customers who rarely do so.
- Monetary factor reflects how much a customer has spent with the brand during the specific period of time. Big spenders having high disposable income should usually be treated differently than customers who spend little on the purchase of the products from the brand.
Steps in RFM Analysis (RFM Segmentation)
The first and foremost step in building an RFM Model is to allocate Recency, Frequency and Monetary values to each and every customer of the firm. The raw customer data used to conduct this step should be readily available in the company’s CRM software or transactional databases can be compiled in an Excel spreadsheet:
- Recency is simply the amount of time since the customer’s most recent transaction with the firm indulging in the purchase of its products.
- Frequency is the total number of transactions made by the customer with the firm in a particular period of time.
- Monetary is the total amount that the customer has spent across all transactions during a particular period of time.
The second step involved dividing the customer list into tiered groups for each of the three dimensions of the model (R, F, and M) using Excel or any another software tool used by the firm. Unless the firm used any specialized software, it is advised to divide the customers into four tiers for each of the dimension of the model so that each the customer group will be assigned to one tier in each of the dimension of the model.
Recency Frequency Monetary
R-Tier-1 (most recent) F-Tier-1 (most frequent) M-Tier-1 (highest spend)
R-Tier-2 F-Tier-2 M-Tier-2
R-Tier-3 F-Tier-3 M-Tier-3
R-Tier-4 (least recent) F-Tier-4 (only one transaction) M-Tier-4 (lowest spend)
This technique results in 64 distinct customer segments (4x4x4), into which customers will be segmented accordingly. Three tiers can also be used that will result in the 27 segments.
The third step of the model involves selecting the groups of customers to whom specific types of communications will be sent, based on the RFM segments in which they appear.
It is quite helpful to assign names to segments of interest of the customer.
- Best Customers – This group consists of the customers who are found in R-Tier-1, F-Tier-1 and M-Tier-1as they have transacted recently, do so often and spend more than other customers of the firm. A shortened notation for this segment is 1-1-1; we’ll use this notation going forward.
- High spending New Customers – This group consists of those customers in the category of 1-4-1 and 1-4-2. These are customers who have transacted only once, but very recently and they spent a lot on the purchase.
- Lowest Spending Active Loyal Customers – This group consists of those customers in the category of 1-1-3 and 1-1-4 and they have transacted recently and often do so but spend the least).
- Churned Best Customers – This segment consists of the customers in the categories of 4-1-1, 4-1-2, 4-2-1 and 4-2-2 and they have transacted frequently and spent a lot, but it’s been a long time since they’ve transacted with the firm.
The fourth step of the RFM Model actually goes beyond the RFM segmentation by crafting specific messaging that is tailored for each customer segments. By focusing on the behavioral patterns of the particular groups of the customers, the RFM Model allows marketers to communicate with customers in a more effective and efficient manner.
- Below mentioned are a few examples for illustration, using the groups named above:
- Best Customers – Communications with this group should make them feel valued and appreciated by the firm. These customers generate a disproportionately high percentage of overall revenues for the firm and thus focusing on keeping them happy should be a top priority of the management. Further studying and analyzing their individual preferences will provide additional opportunities for even more personalized messaging and means of communication.
- High spending New Customers – It is always a good idea to nurture all new customers of the firm because these new customers spent a lot on their first purchase. Like with the Best Customers group, it’s important to make them also feel quite valued and appreciated by giving them terrific incentives to so that they continue to interact with the brand.
- Lowest Spending Active Loyal Customers – These repeat customers are active and loyal, but they there spending capacity is quite low. Marketers should create specific promotional campaigns for this group to make them feel valued and incentivize them to increase their spending levels. As loyal customers, it also pays to reward them with special discount offers so that they also spread the word about the brand to their friends earning the referrals for the firm.
- Churned Best Customers – These are valuable customers for the firm who have stopped transacting a long time ago. While it’s often challenging to re-engage with them for the firm, the high value of these customers makes it worthwhile trying to get them back. Likewise the Best Customers group, it’s vital to communicate with them on the basis of their detailed preferences with the data derived from their earlier transactions.
Problems of RFM Analysis
RFM Model and its segmentation approach is a straightforward and powerful method for customer segmentation. But the RFM Model only looks at three specific factors meaning that the method may be excluding other variables that are equally important such as products purchased, previous purchase history, campaign responses, and other crucial demographic details of the customers.
The RFM Model is a historical method by nature and looks at past customer behavior that may or may not precisely specify future activities, preferences, and responses at risk customers.
The other advanced customer segmentation methods are based on predictive analytics technologies that are far more accurate at segmenting customers and predicting future customer behavior with the firm.
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Thanks to hitesh sir this RFM model is really i opening method in building brands its bad luck this methods never thought in our education system.
Its a great contribution from u