What is RFM Analysis? How to Use it for Customer Segmentation

Smart marketers know the value of “know thy customer.” So instead of just generating more clicks, marketers need to follow the paradigm shift from increased CTRs (Click-Through Rates) to retention, commitment, and developing customer relationships.

Instead of interpreting the entire customer base as a whole, it’s more useful to segment them into similar groups, know the traits of every group, and engage them with appropriate campaigns rather than segmenting on just consumer age or geography.

RFM is one of the most reliable, powerful segmentation,  and simple-to-use methods to empower marketers to analyze customer behavior.

Now you must be wondering what RFM analysis is and how you can leverage it for your business growth.

So, without any further delay, let’s move forward and start from the basics.

What is RFM Analysis?

RFM stands for Recency, Frequency, and Monetary value. These values correspond to crucial customer traits.

Further, these RFM metrics are leading indicators of a customer’s behavior because the frequency and monetary value influence a customer’s lifetime value, and recency influences retention, a measure of engagement.

Businesses that are lacking in the monetary aspect, like a readership, viewership, or surfing-oriented products, could utilize Engagement parameters rather than Monetary factors.

Therefore leads to applying RFE – a variation of RFM. Moreover, this Engagement parameter could be interpreted as a composite value based on several metrics such as bounce rate, the number of pages visited, visit duration time spent per page, etc.

RFM factors explain these facts:

• the more recent the purchase, the more active the customer is in promotions
• the more often the customer purchases, the more engaged and delighted they are
• monetary value distinguishes heavy spenders from low-value purchasers

Customer Segmentation Using RFM Analysis

Whenever you plan to do marketing spending or to formulate a new promotion, retail marketers have to be cautious about segmenting and targeting consumers.

It would be a wastage of marketing spend if, for instance, an ad campaign is targeted towards all the thousands of your consumers. Moreover, such an untargeted marketing promotion is not likely to have a higher conversion rate and may even damage your brand value.

Retailers now employ sophisticated methods to segment their customers and target their marketing efforts to these segments. Under RFM analysis, every customer is scored based on three factors, i.e., Recency, Frequency, and Monetary value.

As a consequence, RFM analysis lets companies recognize customers who are most likely to respond to a new offer.

Let’s have a look at each of these factors in detail:

1. Recency:

Recency is the most significant predictor of who is more likely to acknowledge an offer. Consumers who have newly purchased from you are more likely to buy again from you than those who did not buy recently.

2. Frequency:

The second most crucial factor is how often these customers purchase from you. The higher the frequency, the higher is the odds of them acknowledging your offers.

3. Monetary:

The third factor is the money amount these buyers have spent on purchases. Customers who have paid higher are more likely to buy based on the offer than those who have spent less.

Since we have seen all three factors, it is time to understand how it works.

How Does RFM Analysis Work?

RFM analysis every customer is allocated a score for recency, frequency, and monetary value, and then your total RFM score is calculated.

The recency score is estimated based on the date of their most recent acquisition. The scores are usually categorized based on the values. For instance, a company may develop a category system of 1 to 5, a five being the highest.

Therefore, in this case, consumers who have bought within one month have a recency score of five, and customers who bought within  1-3 months have a score of four, and so on.

Furthermore, the frequency score is estimated based on the number of times the customers bought. Customers with higher frequency get a higher score.

Ultimately, customers are allotted a score based on the amount they spent on their acquisitions. For measuring this score, you may estimate the actual amount spent or the average spent per visit.

By adding all three scores, a final RFM score is calculated. The consumers with the most powerful RFM score have been deemed the ones most likely to answer their offers. 

Customer Segmentation

RFM analysis supports retailers in segmenting the consumers and plan offers and promotions based on their profiles.

Here are a few instances:

• Consumers with an overall high RFM score serve the best customers.
  
  
• Consumers with a high overall RFM score but have a frequency score of 1 are new. The company can give special offers for these consumers to enhance their visits.
  
  
• You can interpret RFM analysis together with other customers’ data, such as their income levels, and gender to segment the customer base.
  
  
• Customers who own a high-frequency score but a low recency score are those consumers that used to visit pretty often but have not been visiting recently. For these consumers, the company is required to give promotions to bring them back to the store or run surveys to discover why they abandoned the store.
  
  
• You can interpret RFM scores together with campaign results to eliminate non-responsive customers and further enhance the campaigns.
  
  
• You can examine RFM analysis scores and the products they buy to create highly targeted offers for all customer segments.

Click here for more about Customer Segmentation

Conclusion

RFM analysis is a compelling approach to help you recognize your best customers and build better-targeted campaigns. However, RFM itself is not enough.

Instead, retailers should create more detailed customer profiles, including their demographics, buying patterns, and behavior, and apply this information in conjunction with RFM to present a better value to customers.