RFM Modelling: Leveraging Customer Purchasing Data

If your aim for 2023 is to decrease churn, increase buyer activation, and provide customers more personalized messaging, then RFM Modelling could be your answer. 

Our team understands how important the customer journey is for our clients. We ensure our clients have the best data models and strategies in place so they can access the information they need, when they need it. 

RFM Modelling is just one way we help our clients understand and maximize their customer base. According to our Data Scientist and Engineer Tom Wamelink, RFM is “a basic and easy to implement segmentation method that gives enormous insights in your customer base” for our clients. 

With RFM Modelling, our clients can have “direction on how to grow value with every single customer…and…gives direction to what your goal could be in communication with your customer,” Tom explains. 

 If you are unsure of how you can activate that insight to gain business value, check out the work we did with Intergamma so they could tap into the added business value from RFM Modelling.

What is RFM Modelling?

Also known as a customer segmentation method, RFM Modelling uses transactional data and purchasing behavior to group your customers within the following three dimensions: 

• Recency: the number of days since last purchase
• Frequency: the number of purchase occasions
• Monetary value: the average amount of purchase occasions

The goal of RFM Modelling is to help businesses effectively analyze the past buying behavior of each customer and to  shape future customer behavior by clustering them within these three dimensions.  

“RFM Modelling coding lives in the data warehouse of transactional data and is scheduled to run every period depending on the client's industry and products being sold…this is just one tool in audience segmentation that you should have to better understand your customer,” adds Tom. 

According to Tom, when used properly  RFM Modelling should be able to “give a lot of strategy to businesses so they can grow their business and revenue and keep customer buying  active.”

Breaking down the data in RFM Modelling

Although RFM Modelling runs every month (for most business cases), how you understand and then visualize the transactional data collected, is what informs ensuing business and marketing activation strategies.

1. Segmentation:

As mentioned above, the first step in using the collected RFM data is to segment the customer data by the three core dimensions in RFM: 

At this point you are also assigning a score for each tier from highest to lowest and using custom-built filters (tiers or groups) such as bought in the last seven days; one month; three months and so on.

2. Groups:

Once you have your customers clustered within the three main dimensions, you further break down the clusters into the tier levels or groups your business has set. For every dimension customers are then appointed to five groups of equal sizes.

"As an example: the F1 group in Frequency dimension represents the top 20% of customers with high frequency and the F5 group is the 20% with the lowest frequency,” explains Tom.

3. Targeting customers using RFM scores:

“Finally, when combining the scores of your customers within the three dimensions and groups, customers can again be appointed to (5x5x5) 125 unique combinations. For example: The RFM 555 group represents your champions: most current Recency, highest Frequency, highest Monetary value.” 

Once your customer data has been clustered, grouped, and scored you can identify the customers you have within the following segments: champion, loyal, recent, and at risk. At this point you can start working on personalized campaigns and messaging for the customers within these segments. 

Who can use RFM Modelling insights?

The transactional data gathered can be put to strategic use by departments that are in charge of delivering personalized communication and marketing campaigns to your customers and also teams running digital marketing campaigns. 

For example, “some company’s only have one-time buyers and RFM Modelling lets you see this so your marketing team can work on strategies to activate these buyers so they become repeat customers,” notes Tom.

RFM can help your team create seamless interactions with high customer satisfaction, helping your customers to feel that your brand understands them and can cater to their need.“RFM provides direction on how to grow value with every single customer…and… gives direction to what your goal could be in communication with your customer,” Tom adds.

In addition, when you run RFM Modelling, customers each get an RFM score based on their ranking and scale within the three dimensions they are grouped in based on their purchase behavior. When customers that have an RFM score visit your website personalized marketing messaging can also be activated. 

“When customers visit a website and are recognized, their corresponding RFM score can be loaded from the data warehouse in real time. Then the website can be optimized for a  personal experience based on the RFM dimensions.” 

Overall, RFM Modelling can inform your customer base and the current differentiators between them along with buying patterns and behavior trends. This data can inform your marketing activation. 

Finally, because you don’t need much data to set up RFM Modelling, it can be used for businesses of all sizes to help them on their journey to growth.

RFM Modelling “really does help businesses grow because it helps identify who to target, current buying patterns, and how to strategize email outreach to retain or reactive customers,” according to Tom.

Summary

When properly coded and implemented in the data warehouse of your transaction data, RFM Modelling can deliver the key insights your business needs to deliver personalized and targeted messaging. 

“How do you want to grow? What does this customer like? What tone do we use? RFM Modelling when combined with other segmentation tools helps inform the answers to these kinds of questions and related personalization for better marketing activation.” 

More importantly, RFM Modelling helps your business lower churn rates. With the RFM model, each segment customers can have their own customer journeys based on personalization, which creates value and establishes loyalty and trust. Through RFM, your business can also identify customers on the verge of churning out and focus on converting them to active customers. 

If you would like to learn more about RFM Modelling and how it can transform how your business understands and communicates with customers, check out our RFM Modelling use case. If your business has yet to implement RFM Modelling for your businesses transactional customer data,  feel free to reach out to us directly to learn more.