simulate

Monte Carlo revenue forecasting simulates thousands of possible outcomes using channel ROI uncertainty and diminishing returns. It requires scenario inputs with per-channel budget allocations, ROI mean and variance estimates, and a multi-month projection horizon to compute probability distributions.

Sensitivity analysis in marketing mix modeling identifies which inputs—such as ROI uncertainty, saturation points, and seasonality—most influence revenue variance. It evaluates top scenarios to highlight drivers of risk and upside across channel-mix changes and budget reallocations.

You calculate target-hit likelihood by running probabilistic simulations that model channel ROI uncertainty and diminishing returns. The simulation outputs probability distributions and percentiles like P10, P50, and P90, showing the exact likelihood of reaching your specified revenue target.

Yes, scenario comparison produces expected value, P10/P50/P90 percentiles, target probability, and risk-adjusted return for multiple budget reallocation scenarios. It also generates dominance flags to identify which channel-mix changes outperform others under ROI uncertainty.

Scenario modeling with diminishing returns requires per-channel budget allocations, ROI mean and variance or calibrated estimates, a multi-month projection horizon, and any revenue targets. It uses these to compute probability distributions and sensitivity-driven recommendations.

Use probabilistic revenue forecasting when evaluating new channel launches, spending constraints, or budget reallocations where ROI uncertainty exists. It replaces single-point estimates by computing probability distributions, percentiles, and sensitivity to show upside, downside, and target-hit likelihood.