Home▸AI & Machine Learning▸Sales Forecasting with Machine Learning: Win-Probability Models and Deal Scoring

📊 Sales Forecasting with Machine Learning

Watch deals flow through a live 3D pipeline, colored by a machine-learned win-probability score, and run a Monte Carlo simulation to see the resulting revenue forecast distribution.

AI & Machine Learning3DAdvanced60 FPS

Deals drift through a 3D pipeline as glowing gems, colored by a machine-learned win-probability score, while a Monte Carlo forecast bank shows the distribution of possible total revenue outcomes.

🔬 What It Demonstrates

Win probability is a logistic function of pipeline stage and deal quality, exactly like a gradient-boosted classifier's calibrated output. Priority score multiplies probability by deal value, and Monte Carlo resampling turns thousands of win/loss draws into a forecast range instead of a single guess.

🎮 How to Use

Adjust pipeline volume, average deal size and model confidence to reshape the flow of deals, set a priority threshold to see which deals a rep should focus on, and run the Monte Carlo simulation to regenerate the revenue forecast histogram.

💡 Did You Know?

Well-calibrated win-probability models are judged by how closely predicted probabilities match realized outcomes — a model that says "70% likely" should actually close about 70% of the time it says so.

⚙ Under the hood

Watch deals flow through a live 3D pipeline, colored by a machine-learned win-probability score, and run a Monte Carlo simulation to see the resulting revenue forecast distribution.

machine learningsales forecastingwin probabilitydeal scoringmonte carlorevenue predictiondata analysisThree.js

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