Monte Carlo Simulation (MCS) is a technique used to explore possible outcomes and their probability. It can be applied in areas such as market sizing, customer lifetime value measurement and can also be used to manage customer relationships.
It involves integrating a number of components to provide a model enabling you to conduct risk analysis. It does so by considering a range of possible outcomes, before calculating how likely it is that each particular outcome will happen.
What do you need in order to run an MCS?
To run a Monte Carlo simulation, a mathematical model of your data is needed – for example, a spreadsheet. Within this data, there must be one or more outputs that you’re interested in measuring, for example profit.
There must also be inputs – these are the factors that may have an effect on your output variable. An example of this could be, if trying to measure profit for instance, the number of sales and total marketing spend.
If you knew the exact values of the inputs, you would be able to easily determine what profit you’d be left with. However, if you’re not sure of these variables, an MCS allows you to calculate all the possible options, as well as the probability of each option.
How does it work?
MCS works by replacing all unknown values with functions which generate samples from distributions determined by you. A series of calculations and recalculations are then actioned, producing models of all the possible outcomes and the probability of them being realised.
What could MCS be used for in marketing analysis?
Customer behaviour and whether or not customers will continue to purchase from a brand in the future is something that nobody can be sure of. This makes estimations of customer future value an arduous task.
Imagine a customer owns a few products from a certain brand. This means they are less likely to purchase similar products than other customers who don’t own anything from a brand. Or does it? They could enjoy these products so much that they’re more inclined to repurchase. MCS would be a good way to measure the possible outcomes of this situation. In this example, this would allow a brand to know whether or not to restock a product.
It seems there is an infinite amount of data analysis techniques out there, and they’re worth exploring. Lesser heard of methods could be the secret to unlocking the knowledge you need to steer your brand in the right direction.
