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RFM Analysis

We have already mentioned this in a previous article: customer information in a CRM is a real treasure trove!

We have also already mentioned the figure from a study published by the Harvard Business Review, which tells us that acquiring a new customer can cost 5 to 25 times more than retaining an existing one. This survey highlights the strategic importance of investing in customer retention.

How to do it? Today we present a method, that of RFM analysis, a very interesting method for companies wishing to optimise the value of their customer portfolio.

Introduction to RFM Analysis

RFM Analysis is a methodology to segment buyers according to their purchasing behaviour. The name itself is derived from the three key variables considered: 

  • Recency (the time distance since the last purchase), 
  • Frequency (how often the customer buys) and 
  • Monetary (how much our customer spends). 

This strategic approach is based on the belief that past purchasing behaviour can predict future behaviour, allowing companies to adopt targeted and customised strategies to maximise customer engagement and profitability.

The Fundamentals of RFM Analysis

a. Recency (R) – the time distance since the last purchase

The variable ‘Recency’ measures the time that has elapsed since a customer’s last purchase. Customers who have made recent purchases are often considered more active and engaged than those who have lost contact with the brand over time.

b. Frequency (F) – How many times the customer has made purchases

The variable ‘Frequency’ focuses on the total number of transactions made by a customer during a certain time period. Customers who make frequent purchases are often the most loyal and likely to continue doing business with the company over time.

c. Monetary (M) – How much the customer spent

The variable ‘Monetary’ assesses the total amount of money spent by a customer during his relationship with the company. This parameter provides a direct indication of the profitability of each customer, allowing companies to concentrate resources on the most profitable customer segments.

We will use these three fundamentals to give a ‘grade’, a ‘score’ or, if you prefer, a ‘ranking’ to our customers. Specifically, we will put the customers in our CRM in order using Regency, Frequency and Monetary as sorting criteria.

Objectives of RFM Analysis

The main objective of RFM Analysis is to create homogeneous customer segments, also known as clusters, in order to identify the most valuable and profitable customers for the company. This segmentation allows companies to adopt customised and targeted approaches to meet the specific needs of each customer group, thereby improving the effectiveness of their marketing and retention strategies.

Implementation of RFM Analysis

a. Breakdown into Quintiles

The RFM Analysis is based on the concept of dividing customers into quintiles, i.e. five groups of equal size based on the scores assigned for the variables Recency, Frequency and Monetary. This process allows a clear and easily interpretable segmentation of the customer base, facilitating the identification of the most relevant opportunities and challenges for the company.

b. Allocation of Scores

To assign scores to customers on each of the RFM variables, companies need to define clear and consistent criteria that reflect their business objectives and the specific characteristics of their industry. These scores are then added together to obtain an overall RFM score for each customer.

Applications of RFM Analysis

a. Decision Support and Forecasting

RFM Analysis provides companies with a valuable decision-making and forecasting tool to optimise their promotion and retention strategies. By identifying the most valuable customers and those at risk of churn, companies can allocate resources more efficiently and purposefully, thereby maximising return on investment and overall customer satisfaction.

b. Identifying Best Customers

Using RFM Analysis, companies can identify their best customers, i.e. those who have made recent, frequent and high monetary value purchases. These customers represent the core of the company’s customer portfolio and deserve preferential treatment to maintain and enhance their loyalty over time.

c. Managing the Risk of Abandonment

Identifying customers at risk of churn is essential to preserving the existing customer base. RFM Analysis enables companies to detect early signs of customer dissatisfaction or churn and take preventive measures to mitigate the risk of loss of value.

d. Customising Promotions

By segmenting customers based on RFM criteria, companies can create highly personalised and targeted promotional offers that resonate directly with the needs and preferences of each customer segment. This approach significantly increases the effectiveness of promotions, generating greater engagement and higher conversion rates.

Conclusions

The resources to invest are always limited, they are always a finite number. And so often one finds oneself investing these resources in looking for new leads, new customers. Rarely do we have the sensitivity to think that perhaps the solution to so many problems is already in our hands, or rather, in our CRM.

RFM Analysis is a very effective tool for companies wishing to maximise the value of their customer portfolio through targeted promotion and retention strategies. By segmenting a customer portfolio correctly, you can identify the most promising opportunities and mitigate the risks associated with losing valuable customers. 

Not doing so… well… can simply be a great opportunity wasted.