Understanding scoring and customer groups in the recency, frequency, and monetary analysis (RFM) report

You will learn

Learn how to the recency, frequency, and monetary scores and customer groups are derived in the RFM report. 

For more information about building and customizing your report, head to our guide on getting started with the RFM report.

Before you begin

Your report needs to:

• Have at least 500 customers who have placed an order.
  Note that this does not refer to total profiles, but rather the number of people who have actually made an order with your business. Note that if this section is on a profile but is blank, Klaviyo doesn’t have enough data on that individual to make a prediction.
• You have an ecommerce integration (e.g., Shopify, BigCommerce, Magento, etc.) or use the Klaviyo API to send placed orders.
• You have at least 180 days of order history and have orders within the last 30 days.
• You have at least some customers who have placed 3 or more orders.

RFM definitions

RFM metrics are common terms used in marketing and data strategies. Within Klaviyo, they are defined and calculated as the following:

• Recency
  How long ago a customer purchased from your company. Recency is expressed as the elapsed time (measured in the number of days) since a last purchase within your report’s time frame.

• Frequency
  How often a customer purchases from your company within your report’s time frame.

• Monetary value
  How much a customer has spent in your account (past purchases).

If you need to refer back to these definitions, you can find them within the RFM report itself. Click Advanced settings > About RFM.

Understanding percentiles, scoring, and customer groups

Percentiles and scoring

For a given customer, Klaviyo will determine their percentile among all customers for recency, frequency, and monetary value. Once those percentiles are determined, Klaviyo then assigns the customer a score of 1-3 for each (recency, frequency, and monetary).

By default, scores will be assigned using thresholds that are unique to your account. These include:

• Recency scoring
  • Customer’s most recent purchase was within the last 180 days: 3 score assigned.
  • Customer’s most recent purchase was within the last 365 days: 2 score assigned.
  • Customer’s most recent purchase was outside of the last 365 days and indicates that they are unlikely to ever purchase again: 1 score assigned.
• Frequency scoring
  • Top 33% (usually 3 or more purchases): 3 score assigned.
  • Middle 33%, but less than the 66 percentile (usually 2 purchases): 2 score assigned.
  • Bottom 33% of users (usually a single purchase): 1 score assigned.
• Monetary scoring
  • Top 33%: 3 score assigned.
  • Middle 33%: 2 score assigned.
  • Bottom 33%: 1 score assigned.

Customer groups

Once a score is determined for each RFM metric, these scores are brought together to determine the customer group.

The model below visualizes how these 3 scores are blended together to determine your potential customer buying group. All RFM metrics and their scores (1-3) can be visualized on this cube and where they fall within each of its sides or quadrants. For example, the top green corner of the model illustrates a customer who has earned the top score of 3 for all RFM metrics.

Once Klaviyo brings these 3 scores together, they are then categorized and lined up to a customer group. This customer group describes their present buying habits and is a helpful way to understand which customers are repeat buyers, who may need a nudge for future purchases, who is potentially a churn-risk, etc. The chart below illustrates this logic including:

• Customer group name
• Groups split (i.e., what individual RFM scores contribute to this group)
• General description of the customers in this group
• Suggested actions for this customer group

Additional resources

Getting started with the recency, frequency, and monetary (RFM) analysis report

How to build a segment using RFM properties

How to strategically use RFM properties in campaigns and flows