Group the data and rank your customers

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RFM analysis does not take into account the products they purchased. If you also want to analyze by product, you can simply add a separate column to your spreadsheet to contain this information.


2. Set a benchmark value to use as a reference point


Determine the base values ​​for R, F, and M. For example, for recency, set the brazil telegram number database time period that you define as recent purchases, such as within a week, within two weeks, within half a month, etc. Also, define values ​​for frequency and monetary categories.

It’s not a problem if you don’t set these values ​​perfectly the first time. Just try to choose these values ​​as a reference point and set them logically.


Finally, you can group the data based on the values ​​you set in step 2. For example, a customer might have a high frequency because they made three purchases in the past year, but a low monetary frequency because they were all inexpensive products.



Then, based on their performance in each category, categorize them as VIP customers, returning customers, new customers, inactive customers, etc.



Once the data is fully understood and analyzed, you can discuss and decide on the best approach to target each type of customer.

 

RFM Analysis Example
 

Some people may have a hard time imagining how to use RFM analysis. Here are two examples of how to actually use RFM analysis.



Put yourself in the mind of your customers



VIP customers, or those who score high in all 3 RFM categories, usually have a strong attachment to the brand and want to feel valued by the company. Therefore, it is wise to provide these important customers with special gifts and services, such as giving them access to new products before regular customers.



These VIP customers don’t need a flash sale to convince them to make a purchase – they’re already loyal buyers and respond better to special, personalized treatment provided directly to them by the brand.



Low currency means lower purchasing power



Generally speaking, customers who only buy cheap products are those with low purchasing power. They are unlikely to make a significant contribution to your company's profits. However, if their frequency is high, then they are still willing to buy, so you can approach this group with discounts and special offers.



On the other hand, if the monetary value is high but the frequency is low, it can be assumed that the customer has a high purchasing power, so it is important to consider why the frequency is not growing and develop a strategy to deal with this metric.

 



Considerations for RFM analysis
 

Before you begin, you should be aware of some limitations of RFM analysis.



Not suitable for products that are not used frequently



This analysis method is not suitable for services or products that customers purchase only a few times in their lifetime. The frequency is too low to obtain accurate data.

The target of RFM analysis should be products that are purchased frequently and they can easily switch to competitor products.

Only surface analysis can be performed

It is not possible to analyze the context of why each customer bought. If you want to take this into account as well, you should combine RFM with another form of customer segmentation.

Don’t forget about inactive customers

In RFM analysis, customers who have recently or frequently purchased are considered VIPs. Therefore, customers who happened not to have purchased during the measured time period, or long-term customers with low frequency, may be evaluated as not worthy of attention.



Be aware of this error space and prepare a different approach for older, less frequent customers so you can keep them engaged and familiar with your brand.