Increase/decrease inputs to evaluate the impact on output.

Build scenarios using preset input changes [±10%, ±20%, ±50%...]

Build scenarios using custom input values [12%, 250, 5,700...]

Run 1,000+ scenarios to quantify uncertainty and predict outcomes.

Create automatically sorted sensitivity charts to rank the main drivers of output.

Create multiple data tables quickly without slowing down Excel.

Run Goal Seek on any cell (including formula cells).

Run Goal Seek to find the input value that makes the output break even.

Run Goal Seek to increase output in preset steps (e.g. +5%, +10% etc).

Run Goal Seek to decrease output in preset steps (e.g. -5%, -10% etc).

Change multiple variables simultaneously to evaluate the impact on output.

Run Goal Seek on multiple variables simultaneously.

Run Pareto analysis to rank the weights of output drivers [using a solver].

Run football field analysis to compare ranges from various [valuation analysis] methods side by side.

Run concentration analysis to identify the top items in a series (e.g. customers, products, spend, etc).

Roll-up financials to Q/HY/FY ends and sense-check using sum, range, min, max, etc and a heat map.

Visualize key model line items to identify trends, outliers or errors.

Applied in finance for modeling asset returns, in quality control for process variations, and in business for forecasting demand.

Applied in forecasting sales with limited data, estimating project completion times, and budgeting using optimistic, most likely, and pessimistic estimates.

Applied in modeling task completion probabilities in project management, estimating success rates in marketing campaigns, and assessing risks in financial portfolios within bounded outcomes.

Applied in project cost estimation with expert input, sales forecasting using three-point estimates, and risk analysis in financial planning.

Applied in insurance for modeling claim amounts over time, in finance for timing of market events, and in operations for analyzing service times in queuing systems.

Applied in simulations requiring equal probability outcomes, random sampling in market research, and equitable resource allocation.

Applied in finance for modeling stock prices and asset returns, in business for income distribution analysis, and in reliability engineering for time-to-failure modeling.

Applied in reliability analysis for product lifespans, in manufacturing for failure rate modeling, and in finance for predicting time to default in credit risk.

Applied in risk management for modeling maximum losses, in resource planning for peak demand analysis, and in finance for estimating worst-case market scenarios.

Applied in assessing minimum sales levels, evaluating minimum performance thresholds, and estimating best-case scenarios in financial planning.

Applied in modeling market penetration rates, analyzing new product adoption, and performing logistic regression in predictive analytics.

Applied in estimating means with small sample sizes, modeling heavy-tailed portfolio returns, and conducting hypothesis testing in finance.

Applied in modeling time between customer arrivals or purchases, estimating time to default in credit risk, and analyzing service times in operations.

Applied in modeling income or wealth distributions, analyzing top customers contributing most revenue, and assessing risk of large insurance claims.

Applied in estimating customer conversion probabilities, quality control pass/fail rates, and modeling success in repeated sales attempts.

Applied in modeling number of customer arrivals or calls, estimating frequency of rare events like defaults, and analyzing transaction counts over time.

Applied in quality control for defect sampling without replacement, market research sampling finite populations, and inventory management with limited stock.

Applied in modeling number of sales attempts to reach a quota, estimating failures before success in quality control, and analyzing customer acquisition efforts.

Applied in modeling number of attempts until first success in sales, estimating time until first customer purchase, and analyzing probability of first default.

Applied in randomly assigning customers to test groups, equitable distribution of resources, and simulating scenarios with equal discrete outcomes.

Applied in modeling customer decisions (purchase or not), analyzing yes/no survey responses, and tracking pass/fail outcomes in quality control.