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What is Sensitivity Analysis?

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Last editedDec 20202 min read

Sensitivity analysis is a financial modelling tool used to analyse how different values of an independent variable affect a particular dependent variable under a certain set of assumptions.

It studies how various sources of uncertainty contribute to the forecast’s overall uncertainty by posing ‘what if’ questions. Sensitivity analysis is used within specific boundaries that depend on one or more input variables, and is implemented in a wide range of fields. 

How Sensitivity Analysis works

Most commonly used by financial analysts and economists, it is also known as what-if or simulation analysis. It can be used to ascertain how interest rates affect bond prices and in making predictions about the share price of publicly traded companies.

The technique allows analysts to establish which variables are more critical than others in affecting a decision. Thus, it offers investors insight into how different variables can impact upon their potential investment returns. The analyst will look at how the variables move, as well as how the target is affected by the input variable.

Sensitivity analysis allows for forecasting using historical data, paving the way for important decisions to be made about businesses, the economy and making investments.

An Example of Sensitivity Analysis

Barry is the head of sales for a small garden centre that sells everything from plants and garden supplies to home products, garden furniture and even pet essentials. He knows that revenue increases every summer as more customers visit in the warmer weather and focus on upkeep for their gardens. But this year he wants to find out exactly how much sales will rise in accordance with higher percentages of customer traffic.

One premium outdoor BBQ set costs $500. Last year during the months of May, June and July the garden centre sold 100 outdoor BBQ sets, bringing in $50,000. 

After conducting a sensitivity analysis, Barry ascertains that a 10% increase in customer visits during the summer will result in a 4% increase in the number of BBQs sold.

With this information, he can predict how much money will be made from different variables of customer visits. Therefore if the percentage of customer visits rises by 10%, 25% or 50% he can expect to see sales increases in line with 4%, 10% or 20%. Check out our guide to cash flow projection for help. 

As the sensitivity analysis emphasises that sales are highly sensitive to changes in customer visits, Barry will be sure to buy in enough extra stock to cover higher levels of footfall over the summer.

Sensitivity Analysis vs scenario analysis

Scenario analysis is often used to examine certain scenarios in detail, such as a stock market crash or change in the nature of a business. After analysing the details of the specific scenario, the analyst changes the variables within the model to align with it. 

This allows the full range of outcomes to be seen, given all extremes, as well as an understanding of what the outcomes would be given a specific set of variables defined by a real-life scenario.

Sensitivity Analysis advantages

The advantages of sensitivity analysis are numerous. Because it’s an in-depth study of all the variables, the predictions are far more reliable. It allows decision-makers to see exactly where they can make improvements and enable people to make sound decisions about companies, the economy or their investments.

Sensitivity analysis is also fairly simple to understand. The numerical outcomes do not favour any particular variables. 

Sensitivity Analysis disadvantages

There are some disadvantages to sensitivity analysis to consider. By using historical data to forecast the effect of variables on outcomes, there is room for error. For example, Barry’s garden centre may not sell as many BBQs this year for reasons not added to the sensitivity analysis, such as cheaper models offered by a competitor at a special discount.

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