What is RFM analysis & segmentation?
Conducting RFM analysis and performing RFM segmentation is a great way to identify and target specific customer segments. It allows businesses to determine customers’ likelihood to buy certain products and services, and assessing their propensity to purchase. This is done by considering recency, frequency, and monetary factors derived from historic behavior and transactional data and predictive analytics.
This enables businesses to adapt their marketing efforts in order to reach the right audience. Based on activity and behavior, RFM segmentation allows you to create intelligent, personalized campaigns that will benefit your ROI.
RFM analysis leads to a number of attributes or metrics that you can then use to analyze customer behavior and value, and to better segment audiences for more predictable results and sales success.
What the RFM stands for in RFM analysis & RFM segmentation
To begin with, it helps to know what RFM stands for. RFM is based on 3 dimensions:
How recent was the customer’s last purchase or activity? This can be a product or service purchase, but also a website visit, for instance. Usually, customers who have interacted with your brand recently are more likely to respond to other communications or promotions you send them.
How often does the customer buy from you? Those that buy from you more often are more engaged and more satisfied. They are your most loyal customers and thus more responsive to promotions. People who only interacted with your brand once, are a separate category.
How much do they spend, what is the total value of the purchases? How much customers spend and the total value of their purchases – both at the point of sale and over time are key factors influencing monetary value. The monetary value of a customer is the total amount the customer has spent to date when divided by how frequently they purchase products and services from you is their average spend per purchase.
Note that in the case of monetary value, is it not always the concrete value of a purchase. This differs from industry to industry. In financial services, for instance, you have to look at the average revenue a product generates across all of your customers or a segment of them (for a credit card, the actual revenue is generated by the percentage rate on the card). This translates into net present value (NPV) and takes into account the average ROI generated by the acquisition of the product across all customers and the average lifespan of the product.
How is RFM Analysis & Segmentation used?
RFM segmentation is widely used today and has been around for more than forty years. It is based on the Pareto Principle that claims that 80% of your business comes from 20% of your customers. On average, the top 5% of customers spend 10 times more than the rest of your database and even represent a third of your total revenue. There are many types of buyers, the ‘champions’ are the ones who interacted or purchased recently, buy often and spend the most; the ‘loyal customers’ buy on a regular basis and respond to promotions; and then there are, for example, also those that don’t buy frequently but usually spend a lot. High-value segments are groups or ‘clusters’ that are important to keep happy if boosting brand advocacy is one of your main priorities.
What are the benefits of RFM analysis and RFM segmentation?
First, you conduct RFM analysis to accurately apply RFM segmentation. Being able to apply this segmentation allows you to sort and see who and where and how your highest value customers are distributed. The RFM analysis will allow you to see how exposed your business is to churn and how much opportunity there is to convert customers. RFM segmentation helps to describe customer behavior and allows contextualized and more effective direct marketing.
Personalized offers lead to more and better quality customer engagements. And when your customers receive something they’re interested in, they are more likely to stay loyal to your brand, leading to reduced churn rates and improved customer lifetime value.
The higher conversion rates and thus business revenue will then generate an overall improvement of ROI and cost-effectiveness. And of course, you are really getting to know your customers by understanding what life stage they’re in. This will, in turn, help with product analysis and development, leading to more effective and successful product launches.
Keep in mind that strategies apply for each tier of customers within RFM segmentation models. Don’t obsess over the highest-value customers because even though it is important to pay special attention to them, don’t lose other customers out of sight.
RFM analysis and RFM segmentation with an Intelligent Engagement Platform
NGDATA’s Customer DNA is a foundational feature of the Intelligent Engagement Platform. This real-time 360 customer view consists of predefined and custom-built metrics that allow you to understand the needs, wants, interests, and preferences of your customers through propensity, engagement, and behavioral scores. Advanced analytics such as RFM metrics allow you to get a complete view of the customer. The data-driven insights extracted from it, give guidance on how to engage with relevant messages.
Discover how the features of NGDATA’s Intelligent Engagement Platform can boost your business.