Monte Carlo simulation for pricing decisions

Chapter 13 explores the use of Monte Carlo simulation as a practical tool for pricing decisions under uncertainty. Since pricing outcomes often depend on variables that are difficult to predict or model analytically, simulation offers a way to estimate possible results by drawing random numbers from probability distributions. The chapter explains how to build and run simulations in Excel using functions like RAND(), RANDBETWEEN(), and NORM.INV() and emphasizes the importance of keeping models simple and focused (the KISS principle). Applications include estimating the probability of breaking even, modeling competitor reactions, and optimizing prices under uncertain conditions. The chapter also demonstrates how to summarize simulation results using data tables, frequency distributions, histograms, and statistical functions. Through detailed examples, including profit simulations for pricing campaigns and willingness-to-pay modeling, readers learn how simulation can guide more informed and risk-aware pricing strategies. The chapter concludes by introducing simulation-based optimization, showing how uncertainty in input variables can be integrated into decision-making models to maximize profit.