RFM (Recency, Frequency, and Monetary Segmentation)

RFM (Recency, Frequency, and Monetary Segmentation)

1. What is RFM (Recency, Frequency, and Monetary Segmentation)?

RFM segmentation categorizes customers based on their purchase behavior using three key dimensions:

  • Recency: How recently a customer made a purchase. A lower value is better, as it indicates a more recent interaction.
  • Frequency: How often a customer makes purchases. A higher value is better, as it indicates frequent interactions.
  • Monetary: How much money a customer spends over time. A higher value is better, as it indicates higher revenue contributions.

This segmentation helps merchants identify customer groups with similar purchasing habits, allowing for tailored marketing and retention strategies.

2. Understanding the RFM Score

The ultimate RFM score is calculated as the average of the Recency, Frequency, and Monetary scores, normalized to a scale of 0 to 100.

  • Higher RFM Scores: Indicate highly valuable customers who purchase frequently, spend generously, and have interacted recently. These customers are your VIPs and should be prioritized with loyalty rewards, premium offers, and retention strategies.
  • Moderate RFM Scores: Represent customers who are engaged but could be spending more or interacting more frequently. These customers can be incentivized with targeted promotions or re-engagement campaigns.
  • Lower RFM Scores: Typically belong to lapsed customers or those with minimal purchases. Consider reactivation campaigns, personalized offers, or gathering feedback to understand barriers to further engagement.

What is a Good RFM Score?
While a “good” score depends on your business and customer base, an RFM score of 70+ often represents a highly engaged and profitable customer. A score below 40 may indicate disengaged customers who need attention.

3. Value to Merchants

  • Identify High-Value Customers:
    RFM highlights loyal customers who buy frequently, spend more, and have shopped recently, enabling merchants to reward them and nurture their loyalty.
  • Create Targeted Campaigns:
    By segmenting customers into actionable groups, merchants can craft personalized campaigns for re-engaging lapsed customers, incentivizing frequent buyers, or converting low-value customers into high-value ones.
  • Optimize Retention Strategies:
    Understanding customer segments based on recency and frequency helps merchants prevent churn and increase lifetime value.

4. Why This Matters to Your Business

AngularView provides unparalleled flexibility in RFM analysis by allowing you to slice and dice RFM scores across dimensions like product categories, geographic regions, or customer demographics. These insights empower merchants to design highly effective strategies.

For instance:

  • RFM + Marketing Subscription Level: Identify how marketing preferences influence RFM scores and refine communication strategies accordingly.
  • RFM + Product Type: Understand which product categories drive high-frequency or high-value purchases, enabling better inventory planning and promotion strategies.
  • RFM + Customer Location: Discover regional variations in RFM scores to optimize local marketing campaigns.

When combined with other measures, RFM segmentation delivers deeper insights:

  • RFM + CLV: Focus on loyal customers with high monetary scores to maximize revenue through retention and upselling strategies.
  • RFM + AOV: Identify segments where high-frequency customers have low AOV and implement targeted campaigns to increase their average spend.
  • RFM + Refund Rates: Detect patterns where high-value customers may have higher return rates and address potential issues to maintain loyalty.

5. How RFM is Calculated

AngularView calculates RFM scores by:

  1. Recency Calculation:
    • The number of days since a customer’s last purchase. A lower score is better.
  2. Frequency Calculation:
    • The total number of purchases made by the customer in the selected time frame. A higher score is better.
  3. Monetary Calculation:
    • The total value of all purchases made by the customer. A higher score is better.
  4. Ranking and Normalization:
    • Customers are ranked within each dimension (Recency, Frequency, and Monetary), and their scores are normalized on a scale of 0 to 100 for consistent comparison across segments.

RFM=(Recency Score + Frequency Score + Monetary Score) / 3

This results in a comprehensive segmentation that enables precise targeting and decision-making.

6. Practical Example of RFM in Action

A merchant identifies a segment of customers with high recency and frequency scores but moderate monetary scores. By combining RFM insights with product type data, the merchant launches a targeted campaign promoting premium products to this group, increasing their average spend and overall revenue.

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