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📊 RFM Segmentation and Churn Prediction

A 3D recency-frequency-monetary cube where each customer is a point clustered into a marketing segment, with a churn-risk boundary you can reshape and a retention campaign you can fire.

AI & Machine Learning3DAdvanced60 FPS🔥 Fire

Every customer becomes a point in a 3D Recency-Frequency-Monetary cube, clustered into marketing segments and scored for churn risk by a lightweight logistic model you can retune live.

🔬 What It Demonstrates

How raw purchase behaviour on three axes collapses into actionable customer segments, and how a churn-probability boundary shifts in that same space as you reweight recency, frequency and monetary signals.

🎮 How to Use

Adjust the recency/frequency weights and churn threshold to reshape the risk plane, switch coloring between RFM segment and churn-risk gradient, then fire a retention campaign and watch high-value at-risk customers get pulled back to safety.

💡 Did You Know?

Recency is so predictive on its own that many retention teams get 80% of a churn model's lift from recency alone, before frequency or monetary features are ever added.

⚙ Under the hood

A 3D recency-frequency-monetary cube where each customer is a point clustered into a marketing segment, with a churn-risk boundary you can reshape and a retention campaign you can fire.

machine learningdata analysiscustomer segmentationchurn predictionmarketing analyticsrecency frequency monetaryThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install