Which mathematical concept is NOT typically used in calculating probabilistic Customer Lifetime Value models in retail analytics?

Customer Lifetime Value (CLV) is a critical metric in retail analytics that relies on sophisticated mathematical models to predict future revenue from customers. The mathematics behind CLV calculations involves probability theory, statistical modeling, and sometimes machine learning algorithms. How well do you understand the mathematical foundations that power these valuable retail prediction tools?

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Markov Chain models for predicting customer purchase sequences
Pareto/NBD (Negative Binomial Distribution) for purchase frequency modeling
Fermats's Last Theorem for customer retention calculations
Gamma-Gamma models for predicting monetary value of future transactions