Customer behavior segmentation – RFM
RFM segmentation is a great method to identify groups of customers for special treatment. Learn how to use this method to improve your customer marketing.
What is RFM Segmentation?
RFM segmentation allows marketers to target specific clusters of customers with communications that are much more relevant for their particular behavior – and thus generate much higher rates of response, plus increased loyalty and customer lifetime value. Like other segmentation methods, RFM segmentation is a powerful way to identify groups of customers for special treatment. RFM stands for recency, frequency and monetary – more about each of these shortly.
Marketers typically have extensive data on their existing customers – such as purchase history, browsing history, prior campaign response patterns and demographics – that can be used to identify specific groups of customers that can be addressed with offers very relevant to each.
While there are countless ways to perform segmentation, RFM analysis is popular for three reasons:
- It utilizes objective, numerical scales that yield a concise and informative high-level depiction of customers.
- It is simple – marketers can use it effectively without the need for data scientists or sophisticated software.
- It is intuitive – the output of this segmentation method is easy to understand and interpret.
What are Recency, Frequency and Monetary?
Underlying the RFM segmentation technique is the idea that marketers can gain an extensive understanding of their customers by analyzing three quantifiable factors. These are:
- Recency: How much time has elapsed since a customer’s last activity or transaction with the brand? Activity is usually a purchase, although variations are sometimes used, e.g., the last visit to a website or use of a mobile app. In most cases, the more recently a customer has interacted or transacted with a brand, the more likely that customer will be responsive to communications from the brand.
- Frequency: How often has a customer transacted or interacted with the brand during a particular period of time? Clearly, customers with frequent activities are more engaged, and probably more loyal, than customers who rarely do so. And one-time-only customers are in a class of their own.
- Monetary: Also referred to as “monetary value,” this factor reflects how much a customer has spent with the brand during a particular period of time. Big spenders should usually be treated differently than customers who spend little. Looking at monetary divided by frequency indicates the average purchase amount – an important secondary factor to consider when segmenting customers.
Why is RFM better than traditional segmentation methods?
The RFM model is built on transactions between the user and the business, to create a robust data-backed method based on hard numbers. This customer data is graded, further analyzed and then segmented in order to target customers as distinct groups. This model helps businesses effectively analyze past buying behavior of each customer, to predict and shape future customer interactions.
Traditional methods of segmentation, used by market research companies before the advent of data analytics, use variables like demographic and psychographic factors to group its customers. Researchers always utilize sample audiences to predict population behavior, which reduces market researchers’ ability to predict user behavior of niche consumer sets and specific customers.
These studies are carried out manually, are dependent on skilled researchers, and are prone to human error. A sample could be incorrect, due to many reasons like an insufficient number of people, incorrect gender balance, varying psychographic factors etc. These problems cannot occur in RFM, as it is a fundamentally data-centric model which analyses the entire population set, instead of a curated sample set. In addition to that, the variables of the RFM model are 100% accurate and precise, whereas traditional research involved factors like psychographics, which could be interpreted subjectively.
Using the RFM model helps a business define interactions with each specific customer, creating opportunities to increase the relevance of messaging, eventually creating the potential for increased customer lifetime value. RFM has the potential to create seamless interactions with high customer satisfaction, helping customers feel that the brand understands them and can effectively cater to their needs at all times.
How RFM helps improve business understanding?
RFM modeling increases a business’ ability to prevent churn by using fundamental marketing principles of Segmentation, Targeting and Positioning, which help understand the following:
Segmentation allows you to divide potential customers into distinct groups allowing businesses to talk to them separately. It helps answer the questions:
- Are all my customers similar?
- What differentiates them from each other?
- Who is my most likely customer?
Targeting involves understanding routines and user behaviour of these segments, allowing you to consider and choose the ideal way to speak to them. It helps answer the questions:
- Where do my customers interact with the brand?
- What’s the best time, place, medium and format to talk to them about my brand?
Positioning helps you understand how to talk about your product/service, in order to maximise customer lifetime value. It helps answer the question:
- What type of brand message will increase and ensure brand trust?
- What type of brand message is likely to induce a purchase interaction?
Principles of Segmentation, Targeting and Positioning have been used since ages in the field of marketing. However with the advent of data analytics, and the creation of number-driven models like RFM, the scope of these principles have widened tremendously. Today, businesses can go beyond the above questions with the help of the RFM model and get answers to highly specific questions such as:
- Who are my best customers?
- Which customer has the potential to buy more?
- Which customer has been churned out/has lapsed?
- Which customer can the business afford to ignore to effectively utilize budgets?
- Which customer can be converted by creating value through promotions?
- Which customer is likely to be loyal in the near future?
The RFM model allows businesses to gain key customer insights, through convenient data collection, and frame business strategy with those insights at the heart of every decision. The model allows the business to gain perspective on what their brand means to existing customers, helps businesses manage customer perceptions, and also translates positive sentiment into purchase opportunities.
Businesses can recognize critical customer segments like churn-risk users, and create a bespoke marketing plan, specifically designed to retain those customers. Simultaneously, a business can also use the RFM model to maximize the potential of active users, by creating personalized messaging and customized offerings, making them feel like high-value customers.
Why does RFM work?
The RFM model is fundamentally built using principles of data-driven marketing. Data-driven marketing has fundamentally transformed how marketing works ever since its inception, as it allows analysis of large sets of customer data like never before. This has led to increased accuracy in understanding customers and enhanced ability to creatively customize messaging. The rise of automation in marketing technology, has led to increased granularity and personalization, leading to enhanced relevance of each brand message.
Origins of RFM
RFM traces its origin back to 1995 when it was cited by Bult and Wansbeek in an issue of Marketing Science. Used in the context of direct mail, it showcased how the three criteria could be used to better estimate demand, reducing costs on printing and shipping, leading to enhanced returns. With the rising sophistication of computing power, RFM has become easier to apply in businesses due to computerized customer histories of today.
Applying Pareto Principle to RFM
The RFM model is linked with the famous Pareto Principle, which says that 80% of total results are driven by the top 20% causes. When applied to marketing, it means that 80% of your total sales are likely to come from your top 20% of users. Regular customers will always be high contributors to business revenue, and hence the retention of those customers is highly critical for business performance.
Role of RFM in Customer Retention
Small businesses constantly face the pressure of acquiring new customers, which define its growth and trajectory and are prone to spending high amounts of money to acquire them. A business cannot sustain itself without customers, and while acquisition is a critical part of business strategy, retention plays a bigger role in ensuring high returns for the business. Customer retention depends on customer satisfaction with the product, service provided by the business, and the interactions the customer has with the business, hence making them feel valued.
Low churn rates are the easiest way to maintain and grow business, as it enables a reliance on customer satisfaction, and also the creation of positive word of mouth by users. The RFM model helps businesses create unique customer journeys for different customer segments, creating value for customers and establishing loyalty and trust.
RFM: Personalisation and focused Use of Marketing Budgets
The digital world is a buyer’s market, with a plethora of options available to a user at their fingertips. Brands are constantly jostling and fighting for a share of the customer’s wallet and attention. In such an atmosphere, understanding customer behavior and segmenting them into distinct groups, help businesses focus their marketing efforts on relevant customers.
With the power of social media at their fingertips to express displeasure and the ease of choosing alternatives, customer expectations regarding the quality of brand interactions are high. Hence creating relevant and personalized messaging, tailored to user behavior has become the norm.
Personalization is one of the major benefits of RFM, as it not only allows you to target different customers with varying but equally relevant messaging, but also gives businesses the ability to recognize changing patterns of user behaviour through the capture of RFM data, and move the customers to other segments if required..
Through RFM, businesses can recognize and focus on converting critical customer segments like customers on the verge of churning out to becoming active users, and also encouraging customers who are loyal to the brand to become ardent followers. By minimizing the waste of resources through effective targeting, RFM helps businesses utilize their marketing budgets wisely and effectively, while also increasing the overall impact of marketing on the business.
The labels we use, will be based on the differing characteristics of the three grades customers have received. As we’ve used 5 (1-5) score segments, and there are 3 criteria, there is a possibility of ((5*5*5) 125 unique segments. Businesses may or may not require 125 distinct segments and can decide the number of scoring segments required and label them, based on the nature of the business. Here are some standard labels which are used:
Let’s describe each of these segments in a bit more detail.
In conclusion, constant improvements in data analytics have ensured that the practical applications of models like RFM are seemingly endless. The RFM model ensures effective marketing practices in a world where creating a customer-centric experience is of utmost importance.
The RFM model, when used in conjunction with traditional models of segmentation, can help businesses visualize new and existing customers differently, and create favorable conditions to maximize customer lifetime value. Finding the right balance between focusing on new and existing customers, along with recognizing behavioral nuances within them, will help businesses create personalized customization, leading to brand trust and loyalty.