🧪 Marketing Market Segmentation Simulation

This simulation illustrates how businesses divide a customer base into targeted segments using k-means clustering on demographic, behavioral or needs-based attributes. A simulated population of customers is plotted as points in a live 3-D feature space; running the k-means algorithm step by step (or on auto-run) moves a set of centroids until each customer lands in the segment it is closest to, converging into clean, addressable groups exactly as a real marketing-analytics pipeline would. Switching the criteria set — demographics, behavior, or needs — reprojects the same customers onto different axes, showing how the "right" segmentation depends on what the campaign is trying to target, while live readouts track inertia, convergence and each segment's share of the market.