Monte Carlo simulation analysis

Quantitative research method

Monte Carlo simulation analysis is a forecasting technique that is useful when there is uncertainty about your market characteristics. For example, when forecasting the uptake of a new drug, you may have conflicting information about the populations size or there may be uncertainty about future reimbursement levels or application areas. This uncertainty is critical in the decision to launch the new drug or not as it might just make the difference between success or failure…

When forecasting potential revenues using the Monte Carlo simulation technique, uncertainty and likelihood of different scenarios are taken into account. As a result, instead of providing one revenue figure, Monte Carlo forecasts predict the likelihood of your revenue to be in a given range.

A forecast result may sound something like this: ‘there is an 80% probability that the capital investment to market the drug will be repaid, and 60% probability that your revenues for the first year will be over 4.89 billion dollars’.

Monte Carlo simulation: benefits & limitations

  • Enables you to make informed decisions in case of uncertainty
  • Provides probability of revenue (or market share, or..) to be in a given range instead of providing one single figure
  • Uncertainty and likelihood information about different scenarios are included when forecasting
  • May also include survey data (e.g. uptake for a new drug measured in a choice-based exercise)
  • Requires informed opinion about likelihood of future scenarios and likely range of variables

Monte Carlo simulation: when to use it?

  • In healthcare: to forecast the uptake of your product when the reimbursement/indication area may change in the future
  • For consumer products: to decide about major capital investments when marketing insight are lacking or hard to find
  • In consumer health: forecast revenue in case of uncertainty about relevant consumer populations
  • For financial service providers: forecast revenues in case of short-term uncertainty of macroeconomic variables (e.g. inflation, interest rates)
  • In telecom industries: when there is uncertainty about future levels of taxation or subsidies (e.g. higher VAT or subsidies towards glass fiber internet connection)