One acronym you might have read about while researching customer data platforms is RFM. It stands for "Recency, Frequency, Monetary Value," and analyzing these data points can provide you with a fuller picture of your customer base.
Keep reading to learn more about the purpose of RFM models and how to conduct an RFM analysis.
RFM is a strategy for analyzing and estimating the value of a customer, based on three data points: Recency (How recently did the customer make a purchase?), Frequency (How often do they purchase), and Monetary Value (How much do they spend?).
Recency: How recently did the customer make a purchase? If they made a purchase recently, the likelihood of them making another purchase is high. However, if the customer hasn't made a purchase in a while, you may need to nurture them with new promotional offers or even reintroduce your brand.
Frequency: How often does the customer make purchases? If they purchase often, you'll know their spending habits and preferences, but if they make one purchase and never return, they could be a good candidate for a customer satisfaction survey.
Monetary Value: How much do your customers spend per purchase? Don't get too caught up on the number here, though — all purchases are valuable. However, the first two letters in the RFM acronym can be visualized more clearly by this third component. If they've made many recent purchases at a high price point, you've got a returning customer that can turn into a brand loyalist.
These three factors of the RFM model can be used to reasonably predict how likely (or unlikely) it is that a customer will re-purchase from a company.
What is RFM analysis?
An RFM analysis evaluates which customers are of highest and lowest value to an organization based on purchase recency, frequency, and monetary value, in order to reasonably predict which customers are more likely to make purchases again in the future.
How to Calculate RFM
Step 1: RFM Scoring
RFM analysis classifies customers with a numerical ranking for each of the three categories, with the ideal customer earning the highest score in each of the three categories. This is known as RFM scoring.
For example, depending on the purchase cycle of your company's product or service you might evaluate customers for recency on a scale of 1-10, with a score of 10 indicating the customer had made a purchase from your company within the last month, and a score of 1 indicating that their last purchase was 10-12 months prior.
Step 2: Run an RFM Analysis
Once a company has decided on its 1-10 scale for each of the three categories, it can review its CRM and give each customer a score for each category. Then, by adding up the three combined scores, companies can run an RFM analysis to determine which companies are most likely to buy again soon, and use that information to prioritize how they're reaching out to and creating value for those high-value customers.
Step 3: Crystalize customer communications.
It's important to note that, while an RFM analysis can provide a quick snapshot of which customers have purchased most recently to prioritize nurturing and loyalty efforts, it doesn't necessarily mean they want to hear all of your offers, all the time. Make sure you still have a clear system in place for customer communication so they aren't constantly getting bombarded with emails and calls from your colleagues. That could alienate them, and eventually make them switch to a competitor. High RFM analysis scores should be a signal to learn from a customer, rather than to try to sell more to them right off the bat.
An RFM Analysis to Grow Your Customer Base
An RFM analysis is simply a tool to give you an idea of how much of your revenue comes from repeat customers vs. new customers, and which levers you can pull to try to make customers happier so they become repeat purchasers. Your company's RFM analysis might indicate that customers aren't satisfied with your product or service after their initial purchase, or that customers who are upsold or cross-sold are more likely to purchase again than other customers.
An RFM analysis helps you find commonalities and differences between customers who repeat purchases and customers who don't to help you learn where there are gaps in your customer experience.
Editor's note: This post was originally published in October 2018 and has been updated for comprehensiveness.