BAYESIAN METHODS IN SALES MODELING: A PROBABILISTIC APPROACH TO FORECASTING AND OPTIMIZATION

This article explores the application of Bayesian inference methods in the field of sales modeling. Moving beyond deterministic models, Bayesian approaches enable the incorporation of uncertainty, prior knowledge, and iterative learning into demand forecasting, conversion prediction, and customer segmentation. The article demonstrates how these models offer enhanced flexibility and interpretability compared to frequentist approaches. Structured examples, including sales funnel analysis and posterior probability adjustments, illustrate the method's real-world relevance. This scientific discussion underscores the strategic value of Bayesian reasoning for digital commerce.