We all want to generate more revenue. Ideally, generate more, with the least amount of effort, if possible. No company wants to invest tens or hundreds of thousands more into marketing activities unless it is going to deliver serious returns. RFM (Recency, Frequency, Monetary) analysis is one way to increase bottom line growth from an existing customer database.
RFM is designed around the 80/20 rule (known as the Pareto Principle), that 80% of your business comes from 20% of your customers. It is this small percentage of your customers who keep you in profit.
Make more from what you have. Those who have been good customers in the past are expected to be good customers in the future. Things do change, but past performance is the most reliable indicator we have of what someone might do in the future.
Based on your existing database customers are giving a ranking: between 1 and 10 (with 10 being highest). The higher the score the more valuable the customer.
- Recency – How recently was the last customer purchase?
- Frequency – How often do they purchase?
- Monetary Value – How much did they spend?
It is a useful method to improve customer segmentation by dividing customers into various groups for future personalization services and to identify customers who are more likely to respond to a promotion or offer.
Clustering is used to group customers with similar RFM values. Customers can be segmented into groups in terms of the period since the last transaction (recency), purchase frequency per year and total purchase expenditure (monetary) per year.
The top group on this scale might include anyone above the average (5,5,5 and above) on all three RFM measurements – meaning a loyal customer who is a frequent, consistent high spender, while the lowest group would be below the average (below 5,5,5,) on all 3 RFM measurements, inactive customers who might need to be “re-awakened”. And there are six others in between based on above or below the average for each RFM value. Each group is a target for tailored content marketing campaigns but with very different objectives.
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How to Measure RFM:
These metrics vary by industry and company, of course. Sales of jeans in Mall in the Midwest are more frequent but less profitable than sales of fighter jets to the Middle East, but for each example there will be high value customers who contribute the top twentieth percentile of revenues.
- Recency – The number of months since a customer last made a purchase (within 12, on average; though of course, if you’re selling fighter jets it could be within the last 5 years).
- Frequency – The number of purchases made within the last 12 months.
- Monetary Value – This largely depends on your own averages. A high amount, for a book store, for example, could be $100 per purchase (given a value of 10). Set monetary values according to what customers spend (up to a score of 10).
The larger and more complete the purchasing history database, the more accurate the figures. Intelligence, across all channels, on customer spending and actions, is essential to assigning accurate scores to an RFM analysis.
Once you’ve got a database broken down into RFM groups (which is an activity you should do regularly enough to ensure it is up to date, at least once a quarter) then you can feed this information into segmentation, automation and behavior driven marketing strategies.
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