RFM analysis (recency, frequency, monetary)
What is RFM (recency, frequency, monetary) analysis?
RFM analysis is a marketing technique used to quantitatively rank and group customers based on the recency, frequency and monetary total of their recent transactions to identify the best customers and perform targeted marketing campaigns. The system assigns each customer numerical scores based on these factors to provide an objective analysis. RFM analysis is based on the marketing adage that "80% of your business comes from 20% of your customers."
RFM analysis ranks each customer on the following factors:
- Recency. How recent was the customer's last purchase? Customers who recently made a purchase will still have the product on their mind and are more likely to purchase or use the product again. Businesses often measure recency in days. But, depending on the product, they may measure it in years, weeks or even hours.
- Frequency. How often did this customer make a purchase in a given period? Customers who purchased once are often are more likely to purchase again. Additionally, first time customers may be good targets for follow-up advertising to convert them into more frequent customers.
- Monetary. How much money did the customer spend in a given period? Customers who spend a lot of money are more likely to spend money in the future and have a high value to a business.
How RFM analysis works
RFM analysis scores customers on each of the three main factors. Generally, a score from 1 to 5 is given, with 5 being the highest. Various implementations of an RFM analysis system may use slightly different values or scaling, however.
The collection of three values for each customer is called an RFM cell. In a simple system, organizations average these values together, then sort customers from highest to lowest to find the most valuable customers. Some businesses, instead of simply averaging the three values, weigh the values differently.
For example, a car dealership may recognize that an average customer is highly unlikely to buy several new cars in a timeframe of just a few years. But a customer who does buy several cars -- a high-frequency customer -- should be highly sought after. So, the dealership may choose to weigh the value of the frequency score accordingly.
RFM analysis is also valuable for organizations that do not sell products directly to customers. Nonprofits and charities can use RFM analysis to find the best donors, for example, as those who have donated in the past are more likely to donate again in the future.
Lastly, businesses that do not rely on direct payments from customers may use different factors in their analysis. For example, websites and apps that value readership, number of views or interaction may use an engagement value instead of monetary value to perform an RFE (recency, frequency, engagement) analysis instead of a standard RFM analysis using the same techniques as the latter.
Segmentation of customers in RFM analysis
RFM analysis is a powerful tool in marketing that helps marketers make the best of their advertising budget.
Instead of simply using an overall RFM average value to identify the best customers, business can use RFM analysis to identify clusters of customers with similar values. Called customer segmentation, this process is used to produce targeted direct marketing campaigns tailored to specific customer types. It enables businesses to use email or direct mail marketing to target messages that a large swath of specific kinds of customers are more likely to respond to.
Some examples of customer types include:
- Whales. The biggest customers with high (5,5,5) values in all three factors that should be targeted with special promotions to keep them active.
- New customers. Customers with high recency and low frequency (5,1,X) are new customers. A targeted follow-up may convert them into repeat customers.
- Lapsed customers. Customers with low recency but high value (1,X,5) were once valuable customers but have since stopped. A targeted message may reactivate them.
How to perform RFM analysis
CRM software may integrate RFM analysis, and many add-on packages exist that can take CRM data and automatically measure the RFM factors and provide graphs and suggestions.
Getting started with RFM analysis can be as simple as using a spreadsheet in Excel, however. Organizations can export a customer's purchase history from a CRM database or direct purchase history into the spreadsheet as raw data, for instance. They would then sort by each of the RFM analysis factors and assign a relative score for each value scaled appropriately for their business.
For example, an ice cream stand may assign monetary scores to customers who spend $0-$5 a 1, $10-$20 a 3, and over $100 a 5; while a car dealership may assign under $5,000 a 1 and over $100,000 a 5. Businesses can then use these scores to create overall customer averages and customer segmentation groupings.
Organizations can also use PowerPivot to create interactive charts to aid in analysis.
Limitations of RFM analysis
Using RFM modeling can provide valuable insights about customers. But it does not take into account many other factors about the customer.
In-depth targeted marketing may also use type of item purchased or customer campaign responses as factors. Customer demographics such as age, sex and ethnicity are not covered in RFM analysis either.
Additionally, RFM only uses historical data about customers and may not predict future customer activity. Predictive methods may be able to identify future customer behavior that RFM analysis cannot.