Which mathematical technique is considered the foundation of modern retail market segmentation that allows retailers to identify distinct consumer groups based on purchase patterns?

In the world of retail data analytics, mathematical innovations have transformed how businesses understand consumer behavior. One particular mathematical technique has become the cornerstone of modern retail segmentation and predictive analytics, revolutionizing how retailers anticipate customer needs and preferences. Can you identify which groundbreaking mathematical approach has had the most significant impact on modern retail analytics?

This is a live public trivia poll on AIPolls.Net. Vote and see real-time results.

K-means clustering algorithm, developed in the 1960s but widely adopted by retailers in the 2000s
Fourier transform analysis, adapted from signal processing to retail in the late 1990s
Euclidean distance minimization, pioneered by Walmart's analytics team in 1985
Monte Carlo simulation modeling, first applied to retail inventory by Amazon in 2003