Data Science

RFM analysis for Customer Segmentation

Smart marketers understand the importance of “know thy customer.” Instead of simply focusing on generating more clicks, marketers must follow the paradigm shift from increased CTRs (Click-Through Rates) to retention, loyalty, and building customer relationships.

Instead of analyzing the entire customer base as a whole, it’s better to segment them into homogeneous groups, understand the traits of each group, and engage them with relevant campaigns rather than segmenting on just customer age or geography.

One of the most popular, easy-to-use, and effective segmentation methods to enable marketers to analyze customer behavior is RFM analysis.

What is RFM Analysis?

RFM stands for Recency, Frequency, and Monetary value, each corresponding to some key customer trait. These RFM metrics are important indicators of a customer’s behavior because frequency and monetary value affects a customer’s lifetime value, and recency affects retention, a measure of engagement.

RFM Metrics

Businesses that lack the monetary aspect, like viewership, readership, or surfing-oriented products, could use Engagement parameters instead of Monetary factors. This results in using RFE – a variation of RFM. Furthermore, this Engagement parameter could be defined as a composite value based on metrics such as bounce rate, visit duration, number of pages visited, time spent per page, etc.

RFM factors illustrate these facts:

  • the more recent the purchase, the more responsive the customer is to promotions
  • the more frequently the customer buys, the more engaged and satisfied they are
  • monetary value differentiates heavy spenders from low-value purchasers
Guide to Automated Segmentation with RFM Analysis

A Quick Start Guide to Automated Segmentation with RFM Analysis

Get access to campaign strategies, pro-tips and recommendations to measure campaign success for each RFM segment.

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How to Implement RFM Analysis Used in Customer Segmentation

RFM analysis helps marketers find answers to the following questions:

  • Who are your best customers?
  • Which of your customers could contribute to your churn rate?
  • Who has the potential to become valuable customers?
  • Which of your customers can be retained?
  • Which of your customers are most likely to respond to engagement campaigns?

Let’s demonstrate how RFM works by considering a sample dataset of customer transactions:

RFM Score Table-1

Table 1: Example Customer transactions dataset

 

Table 1 contains recency, frequency, and monetary values for 15 customers based on their transactions.

RFM Analysis Example

To conduct RFM analysis for this example, let’s see how we can score these customers by ranking them based on each RFM attribute separately.

Assume that we rank these customers from 1-5 using RFM values.

Let’s begin with ranking customers on recency first, as shown in the below table:

RFM Score Table-2

As seen in the above table, we have sorted customers by recency, with the most recent purchasers at the top. Since customers are assigned scores from 1-5, the top 20% of customers (customer 12, 11, 1) receive a recency score of 5, the next 20% (the next 3 customers 15, 2, 7) a score of 4, and so on.

Similarly, we can then sort customers by frequency from most to least frequent, assigning the top 20% a frequency score of 5, etc. For the monetary factor, the top 20% of customers (big spenders) will be assigned a score of 5 and the lowest 20% a score of 1. These F and M scores are summarized below:

RFM Score Table-3

RFM Score

Finally, we can rank these customers by combining their individual R, F, and M rankings to arrive at an aggregated RFM score. This RFM score, displayed in the table below, is simply the average of the individual R, F, and M scores, obtained by giving equal weights to each RFM attribute.

RFM Score Table-4

Recency, Frequency, and Monetary Analysis

The next question that arises is: Is it fair to average out the individual R, F, and M scores for each customer and assign them to RFM segment, as per their purchase or engagement behavior?

Depending on the nature of your businesses, you might increase or decrease the relative importance of each RFM variable to arrive at the final score. For example:

  • In a consumer durables business, the monetary value per transaction is normally high but frequency and recency is low. For example, you can’t expect a customer to purchase a refrigerator or air conditioner on a monthly basis. In this case, a marketer could give more weight to monetary and recency aspects rather than the frequency aspect.
  • In a retail business selling fashion/cosmetics, a customer who searches and purchases products every month will have a higher recency and frequency score than monetary score. Accordingly, the RFM score could be calculated by giving more weight to R and F scores than M.
  • For content apps like Hotstar or Netflix, a binge watcher will have a longer session length than a mainstream consumer watching at regular intervals. For bingers, engagement and frequency could be given more importance than recency, and for mainstreamers, recency and frequency can be given higher weights than engagement to arrive at the RFE score.

This simple approach of scaling customers from 1-5 will result in, at the most, 125 different RFM scores (5x5x5), ranging from 111(lowest) to 555(highest). Each RFM cell will differ in size and vary from one another, in terms of the customer’s key habits, captured in the RFM score. Obviously, marketers can’t analyze all 125 segments individually if each RFM cell is considered a segment, and it’s difficult and overwhelming to visualize this imaginary 3D cube!

In general, the monetary aspect of RFM is viewed as an aggregation metric for summarizing transactions or aggregate visit length. Therefore, these 125 RFM segments are reduced to 25 segments by using just R and F scores.

RFM Model

At CleverTap, we use recency and frequency scores to visualize RFM analysis on a 2-dimensional graph. This enables users to consume and make sense of the scores more easily. Moreover, instead of creating 25 segments, we have combined a few segments to arrive at more manageable and intuitive segments.

RFM grid

As illustrated in the above RFM grid, we can get the following information for each of the segments:

  • brief description of the segment
  • recency (last activity)
  • frequency (activity count)
  • average monetary value
  • reachability of users across different channels

Now, let’s discuss how to interpret the RFM segments to understand the behaviors of those users, and recommend some effective marketing strategies.

Analyzing RFM Segmentation

Let’s delve into few interesting segments:

  • Champions are your best customers, who bought most recently, most often, and are heavy spenders. Reward these customers. They can become early adopters for new products and will help promote your brand.
  • Potential Loyalists are your recent customers with average frequency and who spent a good amount. Offer membership or loyalty programs or recommend related products to upsell them and help them become your Loyalists or Champions.
  • New Customers are your customers who have a high overall RFM score but are not frequent shoppers. Start building relationships with these customers by providing onboarding support and special offers to increase their visits.
  • At Risk Customers are your customers who purchased often and spent big amounts, but haven’t purchased recently. Send them personalized reactivation campaigns to reconnect, and offer renewals and helpful products to encourage another purchase.
  • Can’t Lose Them are customers who used to visit and purchase quite often, but haven’t been visiting recently. Bring them back with relevant promotions, and run surveys to find out what went wrong and avoid losing them to a competitor.

Closing Notes

RFM is a data-driven customer segmentation technique that allows marketers to take tactical decisions. It empowers marketers to quickly identify and segment users into homogeneous groups and target them with differentiated and personalized marketing strategies. This in turn improves user engagement and retention.

In a future blog post, we’ll look at how to use RFM analysis to trend the various segments and alter the behavior of undesirable segments.

Schedule a demo with one of our growth specialists to see it in action.

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