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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.
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.
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:
A Quick Start Guide to Automated Segmentation with RFM Analysis
RFM analysis helps marketers find answers to the following questions:
Let’s demonstrate how RFM works by considering a sample dataset of customer transactions:
Table 1 contains recency, frequency, and monetary values for 15 customers based on their transactions.
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:
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:
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.
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:
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.
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.
As illustrated in the above RFM grid, we can get the following information for each of the segments:
Now, let’s discuss how to interpret the RFM segments to understand the behaviors of those users, and recommend some effective marketing strategies.
Let’s delve into few interesting segments:
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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