RFM 101

About RFM analysis

“RFM” stands for “Recency, Frequency, and Monetary value.” RFM analysis is a widely used technique for segmenting customers into groups based on their behavioral responses to specific messages. The value of RFM analysis is that you can accurately predict the future behavior of customers based on their past behaviors in multiple channels and on other customers with similar behaviors. iPost’s Autotarget uses RFM analysis to improve email campaign success.

The three measures of behavior – Recency, Frequency, and Monetary, are used because of their proven excellence as predictors of future behavior. The more recently a customer has interacted with your brand, the more likely they are to interact with it again. So there is a strong correlation between recency and brand engagement. Next most predictive is frequency: the more frequently a customer has interacted with your brand in the past, the more likely it is that they will interact again in the future. The third strongest correlation is monetary: customers that have spent more money in the past are more likely to interact and spend more with your brand in the future.

Interpreting response by RFM cell

To perform RFM analysis, Autotarget performs a “partitioned sort” on all your customers (that have email addresses) every 24 hours by the recency, frequency, and monetary value of their interactions with your brand, placing them in appropriate cells.

Intuitively, customers with the highest RFM cell values are far more engaged with your brand, and more likely to respond to an offer. iPost’s Autotarget RFM graph shows exactly how many customers per RFM cell actually responded (that is clicked, viewed, and purchased) to a selected mailing(s). Armed with this data, you can quickly and easily create segments of customers based their RFM cell response for follow-on marketing.