How to generate successful customer segments with RFM modelling?Rob Horton, CEO
RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behaviour-based customer segmentation. It enables the marketer to divide customers into various categories or clusters who are more likely to respond to promotions or future personalisation services.
RFM's roots in Direct Marketing
Bult and Wansbeek originally introduced the concept of RFM in 1995. It was used effectively by catalogue marketers to minimise their printing and shipping costs while maximising returns.
The advent of computers made it even easier to perform RFM studies because customer and purchase records were digitised. The logical progression was to take these catalogues online to become websites. An extensive study by Blattberg et al. in 2008 proved RFM’s effectiveness when applied to marketing databases. Numerous other academic studies have also validated the use of RFM in reducing marketing costs and increasing returns.
The power of three
We all know that valuing customers based on a single parameter is flawed. The biggest value customer may have only purchased once two years ago, or the most frequent purchaser may have a value so low that it is almost not profitable to service them. One parameter will never give you an accurate view of your customer base, and you’ll ignore customer lifetime value.
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Calculate the RFM score by attributing a numerical value for each of the criteria. The customer gets more points if they bought in the recent past, bought many times or if the purchase value is larger. Combine these three values to create the RFM score. This RFM score can then be used to segment your customer data platform (CDP).
Customer Segments using RFM Modelling
Analysis of the customer RFM values will create some standard segments, a table of suggested segments is listed below.
Think about what percentage of your existing customers would be in each of these segments and evaluate how effective the recommended marketing action can be for your business.
|Customer Segment||Activity||Actionable Tip|
|Champions||Bought recently, buy often, and spend the most!||Reward them. Can be early adopters for new products. Will promote your brand|
|Loyal Customers||High value, frequent purchasers. Responsive to promotions.||Up-sell higher value products. Ask for reviews. Engage them.|
|Potential Loyalists||Recent customers, but spent a good amount and bought more than once.||Offer membership / loyalty program, recommend other products.|
|Recent Customers||Bought most recently, but not often.||Provide on-boarding support, give them early success, start building relationship.|
|Promising||Recent shoppers, but low value.||Create brand awareness. Offer free trials.|
|About To Sleep||Below average recency, frequency and monetary values. Will lose them if not reactivated.||Share valuable resources, recommend popular products / renewals at discount. Reconnect with them.|
|At Risk||Have spent in larger amounts, more frequently, but not for some time. Need to bring them back!||Send personalised emails to reconnect. Offer renewals and provide helpful resources.|
|Can't Lose Them||Made biggest purchases, and often. But haven't returned for a long time.||Win them back via renewals or newer products. Don't lose them to competition. Talk to them!|
|Hibernating||Last purchase was long ago. Low spenders and a low number of orders.||Offer other recent products and special discounts. Re-create brand value.|
|Lost||Lowest recency, frequency and monetary scores.||Revive interest with reach-out campaign. Otherwise ignore.|
This RFM segmentation will readily answer key questions for your business:
- Who are my best customers?
- Which customers are at the verge of churning?
- Who has the potential to be converted in more profitable customers?
- Who can you view as lost customers?
- Which customers must you retain?
- Who are your loyal customers?
- Which group of customers is most likely to respond to your current campaign?
RFM Score Calculations Simplified
To calculate the RFM score you will first need to identify each customer’s RFM values.
- Recency (R) – period since last purchase.
- Frequency (F) – number of transactions.
- Monetary (M) – total money spent, often called Customer Lifetime Value (CLV)
An example customer may then have a R-value of 1 week, F-value of 5 transactions and an M-value of £2,567 (CLV).
Applying RFM score formula
Once you’ve identified and assigned the RFM values from the purchase history, you can calculate a score for recency, frequency and monetary values individually for each customer.
There are two common options for ranking the RFM values on the scale of 1 to 5:
OPTION 1: SIMPLE FIXED RANGES
Example: If a customer purchased within last 24 hours, assign them 5. In last 3 days, score them 4. Assign 3 if they bought within the current month, 2 for the last six months and 1 for everyone else.
Therefore, the range for each score has been pre-defined. Range thresholds are based on the nature of the business. You would then define ranges for frequency and monetary values in the same way.
You should choose criteria that are relevant to the purchase cycle and product value, e.g. an M-value of £2,500 might seem high, but if you sell holidays at a minimum purchase value of £2,500 it’s not out of the ordinary.
This scoring method depends on the individual business and you must decide what ranges you consider relevant and appropriate..
There are limitations with this option, as the business grows score ranges may need frequent adjustments. What you thought was a high frequency or monetary value in 2020 may be quite different in 2022 once you analyse the data.
This may cause problems with how you deal with your previously high-value customers if they are relatively no longer as valuable as they once were. They may now be incorrectly in the top tier of your loyalty programme.
OPTION 2: QUINTILES – MAKE FIVE EQUAL PARTS BASED ON AVAILABLE VALUES
Quintiles are like percentiles, but instead of dividing the data into 100 parts, you divide it into 5 equal parts. Quintiles work with any industry since the data itself defines the ranges; they distribute customers evenly.
Ultimately, you will want to end up with 5 bands for each of the R, F and M-values, this can be reduced to bands of 3 if the variation of your data values is narrow.
The larger the score for each value the better it is. A final RFM score is calculated simply by combining individual RFM score numbers. There are two ways to do this:
- Addition - this is achieved by simply adding the three scores together, e.g. Mr Jones has a Recency score of 4, a Frequency score of 2 and a Monetary score of 5. His RFM score would then be 4+2+5=11. By using this methodology, the maximum value would be 15 and the minimum is 3.
- Concatenation – this is achieved by linking the three scores together, e.g. Mr Jones has a Recency score of 4, a Frequency score of 2 and a Monetary score of 5. His RFM score would then be 425. By using this methodology, the maximum value would be 555 and the minimum is 111.
The Hive Marketing Cloud Customer Data Platform allows the user to choose either option to grade the RFM values using either fixed or dynamic decoding of the data.
Utilising the RFM Data
There are many different permutations of the R,F & M scores, 125 in total, which is too many to deal with on an individual basis and many will require similar marketing responses. The solution is to segment using the standard 10 personas we suggested earlier.
Each customer is placed into their corresponding segment based on their scores. Frequency and Monetary value are combined to reduce the possible options from 125 to 50. It is logical to combine these as they both relate to how much the customer is buying. Recency is more about the customer re-engagement levels.
The table below shows the destination segments along with the target ranges for Recency and combined Frequency/Monetary scores.
|Customer Segment||Recency Score Range||Frequency & Monetary Combined Score Range|
|Customers Needing Attention||2-3||2-3|
|About To Sleep||2-3||0-2|
|Can't Lose Them||0-1||4-5|
Applying RFM Segmentation to your business
In the brave new world of the Customer Data Platform and email marketing automation, it is possible for a business to use the above segmentation to create automated journeys that nurture customers with relevant and contextual messages that help grow their Lifetime Value and Brand Engagement.
RFM analysis helps your business optimise its marketing operations: better email marketing, higher customer lifetime value, successful new product launches, outstanding user engagement and loyalty, lower churn rate, better ROI on marketing campaigns, success in remarketing, a better understanding of your business, overall higher profits and lower costs.
RFM analysis was first created to optimise spend for direct marketing so that expensive catalogues were not sent to customers who would not convert, and this can now be leveraged in delivering to today’s omnichannel marketing.
Knowing who is most likely to respond to certain messages allows for efficient assigning of budget and identification of the next best action required to optimise Customer Lifetime Value (CLTV).
The Hive Marketing Cloud includes out-of-the-box RFM analysis for your business so you can hit the ground running. If you want to leverage your segmentation to grow customer engagement and value don't hesitate to get in touch.
About the Author
Rob is co-founder and a digital marketing technology expert. He has spent the last 20+ years working with marketing teams of all shapes and sizes, to take their operational marketing needs and deliver successful technology-backed solutions. Rob sets the strategic direction for the company and works closely with clients and partners to ensure we continue to innovate faster than our competitors.
Connect with Rob on LinkedIn.
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