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RFM Segments

Adam z Data Octopus avatar
Written by Adam z Data Octopus
Updated over a month ago

Data View - RFM Segments

The RFM Segments model contains aggregated analytical data regarding customer segments created based on RFM analysis (Recency, Frequency, Monetary). This model enables comprehensive analysis of characteristics, purchasing behaviors, and business value of individual customer segments.

Requirements:

  • E-commerce platform engine: IdoSell

Data Structure and Description:

Segmentation Data

  • Customer Segment - Customer segment determined based on RFM analysis by Data Octopus

Quantitative Metrics

  • Clients Quantity - Number of customers assigned to a given segment

  • Clients Quantity Share - Percentage share of the segment in the entire customer base

Time-based Metrics

  • Avg Days Since First Order - Average number of days that have elapsed since the first order of customers in the segment

  • Avg Days Since Last Order - Average number of days that have elapsed since the last order of customers in the segment

Price Metrics

  • Avg Min Product Net Price - Average net price of the cheapest product purchased by customers in the segment

  • Avg Max Product Net Price - Average net price of the most expensive product purchased by customers in the segment

Order Metrics

  • Orders - Total number of orders completed by customers in the segment

  • Avg Unique Item Count - Average number of unique products purchased by customers in the segment

Financial Metrics

  • Total Net Revenue - Total net value of all orders completed by customers in the segment

  • Avg Order Net Revenue - Average net value of orders from customers in the given segment

Purchase Preference Metrics

  • Avg Quantity Of Purchased Brands - Average number of unique brands purchased by customers in the segment

  • Avg Quantity Of Purchased Categories - Average number of unique product categories purchased by customers in the segment

Customer Segment Characteristics

Champions

RFM Scores: Recency 5, Frequency 5, Monetary 5

The most valuable customers who made purchases recently, buy frequently, and spend the highest amounts. These are active brand enthusiasts who regularly return and generate the highest revenue.

Loyal Customers

RFM Scores: Recency 4-5, Frequency 4-5, Monetary 3-5

Regular customers with high purchase frequency and good order values. They don't always spend the most, but regularly return and constitute a solid customer base.

Slipping Champions

RFM Scores: Recency 1-2, Frequency 4-5, Monetary 4-5

Former Champions who have stopped purchasing. Historically very valuable customers with high frequency and purchase value, whose activity has significantly decreased and require immediate attention.

Big Spenders

RFM Scores: Recency 2-5, Frequency 2-5, Monetary 4-5

Customers with high monetary value with varying patterns of frequency and purchase recency. They spend significant amounts during each purchase, regardless of purchase regularity.

New Customers

RFM Scores: Recency 5, Frequency 1-2, Monetary 1-2

New customers who have just begun their journey with the brand. They made a purchase very recently but have low frequency and purchase value due to their short history.

Potential Loyalists

RFM Scores: Recency 4-5, Frequency 2-3, Monetary 2-3

Recently active customers with increasing purchase frequency. They show potential to become loyal customers with proper relationship nurturing.

Promising Customers

RFM Scores: Recency 3-4, Frequency 1-3, Monetary 1-3

Moderately active customers with low or medium purchase frequency and value. They have development potential and need encouragement for greater engagement.

Frequent Low Spenders

RFM Scores: Recency 1-4, Frequency 4, Monetary 1-3

Loyal customers who buy frequently but spend little during each transaction. They are characterized by high purchase frequency with low order value.

One-Time Big Spenders

RFM Scores: Recency 2-5, Frequency 1, Monetary 3-5

Customers who made one significant purchase but never returned. High-value potential customers requiring reactivation.

Cannot Lose Them

RFM Scores: Recency 1, Frequency 1-3, Monetary 4-5

Customers with high monetary value who haven't purchased recently. They have a history of high spending but are currently inactive - their reactivation is critical for the business.

At Risk

RFM Scores: Recency 2-3, Frequency 3-5, Monetary 3

Previously engaged customers showing signs of declining interest. They have moderate recency, high frequency, but decreasing purchase value.

Need Attention

RFM Scores: Recency 5, Frequency 3-5, Monetary 1-2

Active and frequent buyers, but with low spending per transaction. They could spend more with appropriate incentives and value demonstration.

Hibernating

RFM Scores: Recency 1-2, Frequency 1-3, Monetary 1-3

Inactive customers with low value who haven't purchased for a longer period. They have low purchase frequency and low historical value.

Lost

RFM Scores: Recency 1, Frequency 1, Monetary 1

Customers with the lowest scores in all RFM categories. They have most likely left permanently - a very long time has passed since their last purchase, they bought rarely and spent little.

Application

The RFM Segments Data View is primarily used for:

  • Strategic comparative analysis of individual customer segments in terms of their business value

  • Identification of the most valuable customer segments generating the highest revenue

  • Analysis of price preferences of individual segments (based on average minimum and maximum prices)

  • Research of differences in purchasing behaviors between segments (frequency, value, diversity)

  • Optimization of marketing and pricing strategies for specific customer segments

  • Design of personalized remarketing campaigns tailored to the characteristics of a given segment

  • Evaluation of the effectiveness of retention activities for at-risk segments (At Risk, Slipping Champions)

The RFM Segments model enables strategic marketing decisions based on solid analytical data, which translates into increased effectiveness of marketing activities, better matching of offers to the needs of customers from individual segments, and optimization of advertising budgets through concentration on the most valuable customer groups.

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