Which mathematical model is most commonly used by advanced retailers to predict Customer Lifetime Value (CLV) when working with incomplete customer purchase history data?

In the intersection of mathematics and retail analytics lies a powerful world of customer retention formulas and predictive models. This trivia challenges your knowledge about how mathematical concepts are applied in customer lifetime value calculations - a critical metric that influences product development, marketing strategies, and business forecasting. Can you identify the correct mathematical approach used by major retailers?

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Pareto/NBD (Negative Binomial Distribution) Model
Simple Historical Average Method
Linear Regression Projection
Monte Carlo Markov Chain Simulation