Pricing Methodology
Monte Carlo Simulation
A computational technique that uses repeated random sampling to model the probability of different outcomes in uncertain processes, applied to pricing to forecast margin distributions.
Definition
Monte Carlo simulation is the standard tool for quantifying uncertainty in pricing decisions. For each candidate price, the model runs thousands of simulations, each drawing random values for scope creep, effort overrun, discount probability, and win/loss outcome from their respective probability distributions.
The output is not a single "expected margin" number, but a full probability distribution. This allows agency leaders to answer questions like: "What is the probability that this project will lose money?" or "What is the 90th percentile worst-case margin?"
In agency pricing, Monte Carlo simulation typically reveals that 20-30% of fixed-price projects have a non-trivial probability of negative margin — a fact hidden by deterministic spreadsheet estimates. The simulation quantifies exactly how much risk premium needs to be added to each project type to achieve target profitability with statistical confidence.
The technique originated in nuclear weapons research (Manhattan Project) and is now standard in finance, insurance, and project management. Its application to B2B service pricing is recent and represents a significant competitive advantage for early adopters.
Related terms
Bayesian Pricing
A pricing methodology that uses Bayesian inference to estimate win probability and optimal price points based on prior data and ongoing negotiation signals.
Risk Premium
The additional margin built into a fixed-price quote to compensate the agency for the probability of scope overrun, uncertainty, and unforeseen complications.
Bayesian Analysis
A statistical method that updates the probability estimate for an outcome as new evidence or data becomes available, used in pricing to refine win probability and risk estimates with every new deal.
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