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What is RFM (Analysis)?

Definition, examples, and more

Definition

A segmentation framework that scores users based on how recently they were active, how frequently they use the app, and how much they've spent. RFM helps identify power users, churn risks, and upgrade candidates for more targeted messaging.

How to Calculate

Score each dimension 1-5 based on quintiles. RFM Segment = R + F + M scores combined. Example: R=5 (active yesterday), F=4 (12 sessions/month), M=3 ($50 total spend) = segment 5-4-3.

Example

A fitness app scores users 1-5 on Recency (last session), Frequency (sessions/month), and Monetary (total spend). Users scoring 5-5-5 are power users targeted for annual upsells. Users scoring 1-1-5 (high spenders who stopped engaging) get urgent win-back campaigns.

Why RFM (Analysis) Matters

RFM turns a flat user list into actionable segments. A meditation app used RFM to identify 2,000 high-value at-risk users (high monetary, declining recency) and ran a targeted campaign saving 400 subscribers worth $48K in annual revenue — all from a single segmentation exercise.

Frequently Asked Questions

How do I implement RFM for my app?

Score users 1-5 on each dimension using quintile breakpoints from your data. Create key segments: Champions (5-5-5), Loyal (4-4-X), At Risk (1-X-4+), and Lost (1-1-1). Target each with appropriate campaigns.

What actions should I take per RFM segment?

Champions: ask for reviews/referrals. Loyal: upsell to annual. At Risk: re-engagement campaigns. New High-Value: accelerate onboarding. Lost Low-Value: deprioritize spend.

How often should I update RFM scores?

Weekly for dynamic campaigns, monthly for strategic planning. Automate the scoring with your analytics tool or Botsi to keep segments current without manual effort.

Category
Subscription App Terminology
Related Area
Mobile App Growth & Monetization

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