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RFM analysis for Customer Segmentation
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RFM analysis for Customer Segmentation

Every Marketing professional today wants to craft goal-oriented and targeted marketing content.

Fortunately, the fast-growing world of marketing technology today keeps offering newer and better tools to interact with your customers. But let’s face it, even with the latest technologies, you have to struggle to get critical behavioral indicators and deduce actionable insights from the metrics at your disposal. Is there a way to conceptualize, validate, or measure customer engagement more accurately? Yes. Effective marketers, use RFA Analysis (Recency Frequency, Monetary value analysis) to segment customers accurately. You can too.

The more accurately you segment customers, the more effectively you personalize your marketing and produce better outcomes – better RoI. The Pareto principle, or the 80-20 rule, says 80% of a brand’s revenue comes from 20% of customers.

It’s a lot easier to craft well-targeted achievable goal-oriented, marketing campaigns when you have answers to the really important questions about your customers – questions such as:

– Who are my most loyal customers?

– Who are my highest paying customers?

– Who are my one-time customers?

– Which customers are likely to churn?

RFM analysis gets answers to those questions. These RFM Analysis insights are mapped to extract the metrics that matter the most — retention, loyalty, and customer relationship. Let’s dive deep into what RFM is, and how it works.

What is RFM analysis?

RFM is a well-established customer segmentation method. The acronym stands for Recency, Frequency, and Monetary value. With RFM you can segment customers into specific homogeneous groups. You can then address each customer group with different targeted marketing campaigns according to their current level of engagement.

To elaborate further:

Recency (R) – shows how long it’s been since a customer placed an order.

Frequency(F) – shows how often they order from your store.

Monetary value(M) – shows the total amount they spent on your business.

Based on your business dynamics, define a threshold for each of the three parameters and grade customers on these parameters. For example, customers who bought in the recent past get the highest points, customers who buy often get another score, and customers who spend a lot get yet another kind of score. The combined scores give you the RFM score.

Why should you use RFM analysis?

Before we understand the RFM Analysis process, let’s first take a look at how the data points and results from an RFM Analysis. More generally, it’ll identify your best customers, customers likely to churn and those who can be retained, customers who have the potential to become valuable customers, and those who are likely to respond to campaigns.

It also helps in calculating and improving the customer lifetime value. RFM analysis makes it simple to analyze the customer groups individually and determine the CLV (Customer Lifetime Value)  for each. Once you have your CLV-based segments, you can send your upsell and cross-sell campaigns to the segments that are most likely to respond. One of the most effective campaign ideas to boost CLV and relationships to promote new products to loyal customers who are likely to become your brand advocates. RFM-based segmentation also enables you to focus on smaller segments of customers, thus reducing marketing costs and increasing ROI.

What are the steps to calculate the response rate in RFM analysis?

To understand this, let’s take an example; if we see the data in the last few months, the RFM will look something like the below

Name

R

F

M

Customer 1
01/01
2
$500
Customer 2
03/04
10
$100
Customer 3
10/24
5
$150

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

Name

R

F

M

Rank

Score

Customer 1
1/1/2022
2
$500
1
1
Customer 2
3/4/2022
10
$100
2
2
Customer 3
10/24/2022
5
$150
3
3

Next, let’s score the customers on frequency, as shown in the table below:

Name

R

F

M

Rank

Score

Customer 1
1/1/2022
2
$500
3
1
Customer 2
3/4/2022
10
$100
1
3
Customer 3
10/24/2022
5
$150
2
2

Now, let’s score the customers on monetary value, as shown in the table below:

Name

R

F

M

Rank

Score

Customer 1
1/1/2022
2
$500
1
3
Customer 2
3/4/2022
10
$100
2
1
Customer 3
10/24/2022
5
$150
3
2

Finally, let’s check the total scores.

Name

ScoreR

ScoreF

ScoreM

Total Score

Customer 1
1
1
3
5
Customer 2
2
3
1
6
Customer 3
3
2
2
7

How to Implement the results from RFM Analysis?

Depending on your business objectives, increase or decrease the relative importance of each RFM variable to arrive at the final score. For example, ideal customers for e-commerce companies are generally the most recent ones compared to the date of study (our reference date) who are frequent and spend enough. While in a sector like consumer durables, the monetary value per transaction is normally high, but frequency and recency are low. Hence, in e-commerce companies, the RFM score could be calculated to give more weight to R and F scores than to M.

The simple approach of scaling customers from 1-3 will result in, at the most, 27 different RFM scores (3x3x3), ranging from 111(lowest) to 333 (highest). Each RFM cell will differ in size and vary in terms of the customer’s key habits captured in the RFM score. Marketers can’t analyze all 27 segments individually if each RFM cell is considered a segment. This is difficult and overwhelming!

Thus, based on your business goal, we categorize and create segments. For example, 3 baskets of recency and frequency create 9 segments (3 x 3). Customers are assigned each segment by their respective frequency and recency score.

Need attention
Recency – 1
Frequency – 3
Loyal
Recency – 2
Frequency – 3
Star
Recency – 3
Frequency – 3
At-Risk
Recency – 1
Frequency – 2
On the Fence
Recency – 2
Frequency – 2
Promising
Recency – 3
Frequency – 2
Dormant
Recency – 1
Frequency – 1
Hesitant
Recency – 2
Frequency – 1
Novice
Recency – 3
Frequency – 1

Similarly categorize other segments by taking various combinations of RFM like monetary value and frequency into account. 

At Netcore Cloud, we use recency and frequency scores to visualize RFM analysis on a 2-dimensional graph like this one so you can consume and make sense of the scores more easily. We designed this graph to give you a very intuitive experience.

In conclusion

RFM is a data-driven analysis that can be the heart of your whole digital marketing system, pumping lifeblood into every campaign across channels. With RFM analysis you can easily identify and segment customers into homogeneous groups and then target each group with a personalized marketing strategy.

Once you have integrated RFM segmentation into your website, you can direct all your focus on designing impactful and creative content; rest assured that it will reach the right user. Get in touch with us to understand how Netcore’s RFM model can aid your growth journey!

Schedule a demo with a Netcore specialist in RFM Analysis to view the technology in action with your own unique datasets.

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