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Using RFM Analysis

Using RFM to Understand Your Best Customers and Find Others Like Them

Brent Doering

Although you may not be acquainted with recency, frequency, monetary value (RFM) analysis, I’m betting that most of you are very familiar with the 80/20 rule (or more precisely, the Pareto Principle). Regardless of how you refer to it, it states that for many events roughly 80% of the effects come from 20% of the causes. Of course, in business it’s all about 80% of your sales coming from 20% of your customers. You essentially remain in business due to the support of a small portion of your existing customer base―your best customers.

At IWCO Direct, we view RFM analysis as an initial step in using analytics as part of the decision-making process. It’s certainly not the holy grail of predictive tools, but it is a technique that will help you identify your best customers. What attributes do your best customers have in common that make them… well, your best customers? Obviously it makes a lot of sense to better understand who these folks are so you can a) retain your best customers and b) create new customers by targeting your marketing and prospecting efforts toward those whom most resemble them.

Identifying Best Customers Through RFM Analysis

So how do we identify the best customers? This is where RFM analysis enters our discussion. The idea behind RFM is simple, straightforward, and split into three distinct customer purchasing attributes:

  • Recency: Customers who have purchased from you recently are more apt to purchase from you again compared to customers you haven’t seen in a while.
  • Frequency: Customers who purchase from you most often are more apt to do it again.
  • Monetary value: Customers who spend more with you are more likely to purchase than customers who spend less.

We use RFM analysis to segment customers based on these three categories, to provide you with a picture of their purchasing behavior. However, each of these categories is not necessarily of equal value in highlighting your best customers. Actually, recency is most important. Why? The longer it takes for a customer to repeat a purchase, the less likely they are to return. Frequency and monetary value are more easily resolved; getting a good customer to come back more often and/or to spend more is much more easily accomplished than getting good customers to come back after they stop purchasing altogether.

Although recency is most important, it cannot “go it alone.” Frequency and monetary value factors need to be added into the mix in order to get a well-rounded picture of your customers. In order to do this, an RFM score is assigned to each customer. For the purposes of this article, I’m not going to delve into the scoring calculation, but at a basic level, customers are assigned a score based on these three attributes and essentially ranked from highest to lowest value. The higher the customer’s score, the more likely they are to purchase from you again. The score is assigned based on business rules and/or sorting RFM attributes into categories. This link does a nice job of explaining the scoring calculation.

The Limitations of RFM Analysis

Now that you’ve assigned RFM scores, you have a quantifiable methodology for identifying your best customers. It’s now time to analyze purchasing behavior and uncover insights that you can use to help further understand these best customers AND look to acquire new customers with these same characteristics/attributes. As valuable as these scores can be, it’s also important to understand the limitations that inherently come with this technique.

As exciting as it may be to now have RFM scores assigned to your database and a much more systematic approach for targeting your marketing efforts, it’s key to recognize and appreciate some of the limitations of RFM, such as the fact that it does not take customer behavior into consideration beyond RFM, account for seasonal purchasing, or predict response.

How to Make RFM Analysis Work for Your Direct Marketing

RFM analysis certainly has a place in identifying your best customers and, even more importantly, helping you better understand those purchasing attributes that distinguish your best customers. As a purchasing predictive tool, RFM will provide you with a historic snapshot of what your customers are like, allow you to make more informed decisions on the most responsive segments within your database, and help you identify certain traits and behaviors that you can leverage in the future. However, since the model only relies on these variables, it should only be the starting point in identifying the factors that help predict customer purchases.

IWCO Direct assists many of our clients with their data mining efforts, and one size does not fit all. Please reach out if you’d like to have a conversation regarding your database needs and how IWCO Direct can help you take that first step in the analytics process. And if you’ve already taken this step, we’ll be happy to offer next steps as you look to capitalize on the full potential of your data mining efforts.

link https://www.iwco.com/blog/2017/04/19/rfm-analysis-customer-acquisition/
Brent Doering

Author

Brent Doering

Senior Customer Engagement Manager and graduate of Saint John’s University. Marketing veteran with more than 15 years of leadership experience. Recognized with an Asters Judge’s Choice Healthcare Marketing Award for his efforts to make a difference in people’s lives. Bringing the philosophy of “business is all about relationships,” he approaches each project with inspiration and enthusiasm that empowers his teammates to be at their best. Proud dad, fisherman, hockey enthusiast (North Stars followed by the MN Wild) and tri-athlete.

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