Which mathematical modeling technique revolutionized retail customer lifetime value (CLV) calculations by allowing retailers to predict future purchasing patterns from sparse historical data?

Data analytics has revolutionized the retail industry, with mathematical models predicting consumer behavior with increasing accuracy. Behind these powerful prediction systems are specific mathematical techniques that extract actionable insights from massive datasets. Can you identify which mathematical approach has been most transformative in understanding customer lifetime value (CLV) calculations for mathematics-based retail analytics firms?

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Markov Chain Models - allowing prediction of purchase probability based on transition states between customer interactions
Monte Carlo Simulations - applying randomized sampling to generate purchase probability distributions
Fourier Transform Analysis - identifying cyclical purchasing patterns through frequency domain conversion
Bayesian Belief Networks - creating conditional probability trees based on demographic attributes