RFM analysis is a customer segmentation framework that scores each customer across three dimensions: Recency (how recently they purchased), Frequency (how often they purchase), and Monetary value (how much they spend). By combining these three scores, RFM produces a multidimensional view of your customer base that is far more actionable than any single metric alone - revealing not just who your best customers are, but who is at risk of lapsing, who is showing early signs of high value, and who has already churned.
The practical output of an RFM analysis is a set of customer segments that each warrant a different marketing response. Champions (high R, high F, high M) are your best customers - they bought recently, buy often, and spend the most. They deserve VIP treatment, early access, and loyalty rewards. At-risk customers (high F and M, but declining R) were once Champions but are showing signs of disengagement - this segment is your highest-priority winback target, since they have proven willingness to spend and you are still within reach. Promising customers (recent first purchase, low frequency) are new buyers who have shown initial interest but haven't yet formed a habit - the post-purchase flow and second-purchase incentives are designed specifically for this group. Lost customers (low R, any F and M) have stopped engaging - a suppression decision or low-cost reactivation campaign is typically more appropriate than continued full-price marketing spend.
For Shopify brands using Klaviyo, RFM segmentation can be built directly using Klaviyo's predicted CLV, purchase date, and order count properties - without exporting data or using additional tools. The most common implementation assigns each customer to one of five to seven named segments that are updated dynamically, then maps each segment to a specific email and SMS treatment. This ensures that Champions receive communications that reinforce their status and loyalty, while At-Risk customers receive re-engagement sequences before they're permanently lost - and the entire system runs automatically as customer behavior changes over time.
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