Customer Segmentation using RFM Analysis | Xeno

Originally Published on Times of India

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We all see marketers going crazy behind click-through rates, impression, reach, and other similar metrics like this.

But what’s the point of all these metrics if you don’t know your customers?

Someone has rightly said, Marketers who understand the value of "knowing thy customer" are more likely to succeed.

Now, what can you do as a Marketer: Instead of focusing just on increasing click-through rates, marketers must move their focus from greater CTRs to client retention, loyalty, and relationship building.

Once you understand your customers, 

  • You can understand their likings & their preferences 
  • Segment customers in a marketing-ready format  
  • Run relevant & personalized marketing activities.

Let’s say, you have developed a deeper customer understanding. The next step is moving towards segmentation.

Segmentation is the buzzword amongst marketers today. However, most of the campaigns are segmented only on the basis of age or geography. How can you take it to the next level? The answer is RFM Analysis. 

What is RFM Analysis?

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

It is a marketing technique used to quantitatively rank and group customers based on the recency, frequency, and monetary total of their recent transactions to identify the best customers and perform targeted marketing campaigns. RFM stands for:

  1. Recency: How recently a customer has visited or purchased with a brand
  1. Frequency: The frequency of the customer transactions or visits
  1. Monetary Value: The intention of customer to spend or purchasing power of the customer

Why is there a need for RFM Analysis?

There is a need for RFM analysis as it helps marketers find answers to the following important 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?

The technique enables marketers to make tactical decisions. It enables marketers to quickly identify and segment users into homogeneous groups so that differentiated and personalized marketing strategies can be targeted to them. As a result, user engagement and retention improve.

Let’s demonstrate how RFM works using a simple example:

Oendrila made here last purchase 2 years ago that means she has now turned dormant. 

Now, to reactivate Oendrila as a customer, marketing efforts might be made to remind her that it's been a while since the last transaction, while offering them an incentive to resume buying. In this example, we have used recency as a factor to determine the marketing communication for Oendrila. 

Benefits of RFM Analysis

  • Increase in Customer Retention & LTV

RFM analysis enables marketers to increase revenue by targeting specific groups of existing customers with messages and offers that are more likely to be relevant based on their customer persona.  As a result, response rates, customer retention, customer satisfaction, and customer lifetime value all increase (CLTV).

  • More Loyal Customers

Although you have some customers who buy from you, they are not entirely loyal to you. That is, you may need to make them feel special and pay attention to your loyal customer. This is when RFM helps you which helps determine this loyal audience for your marketing activities. 

  • Reducing Churn Rate

It is no secret that retaining customers is less expensive than acquiring new ones. RFM segmentation allows you to identify customers who are about to churn and take action before they abandon you. 

  • Increasing Sales

Isn't increasing sales one of the most effective ways to boost revenue? RFM analysis can help you in determining which of your customers should be targeted.

Things to take care of while using RFM Analysis 

While RFM segmentation is powerful, it does have limits. When performed manually, it’s prone to human error. RFM analysis is also based on just a few behavioral traits, lacking the power of the advanced predictive analytics now available.

Some businesses may use RFM analysis as an excuse to bombard high-ranking customers with messages and thus reduce response rates on campaigns that could otherwise be highly effective. On the other hand, it can cause marketers to neglect customers with low rankings even though many of them may be worth cultivating. For example, your RFM model may fail to account for the impact of past promotions or seasonality on RFM analysis. Likewise, a customer may have very little activity with your brand one month, yet be ready to engage in purchasing behavior the following month due to a birthday or anniversary.

Summing up, RFM analysis is very relevant in today’s day and time and also remains a perennial favorite of marketers. It has the ability to provide actionable insights down to the individual customer level — all without the assistance of data scientists or complex tools. Whether small businesses or enterprises all organizations should consider RFM Analysis for a better understanding of their customer and growth of their company.

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