Mathematically Modeling Market Projections

Projecting the market is fantastic when you can do it. Companies spend a lot of time trying to look into the crystal ball and find some future. Using simulation allows businesses to figure out what will happen under different circumstances (Chopra, 2017). The ability of companies to project means they will need a number of different data sets and each variable in the projection should be supported by market information.

This means that each variable needs lots of historical data in order to make a type of prediction of what would happen under specific circumstances. The history of the data will need be collected and analyzed for trends. In turn, those trends will show what might happen under certain circumstances.

It isn't so easy as this though as this can take a long time to collect. One must look to the past, other research, do a lot of calculations to isolate the variable, and sometimes may need to experiment with that variable to see if it changes.

You may also find that multiple variables really increase complexity exponentially. For example, understanding product price under certain market conditions will require some knowledge of multiple market conditions and their impact on price of similar type of products.

In our price example, you may also need to look toward macro economics and how general fluctuations, in combination with specific market influence, lead to price changes. As the general market changes is there a suppression in all prices, only some types of products, etc...?

Therefore, solid projections that can be catered to multiple events are difficult to complete because of the amount of data. The internal, external (micro and macro), as well as government regulations/treaties all need to be taken into account. Despite this, we are in the world of big data and it is now possible to collect that data through multiple streams and create ever increasingly accurate projections. Just don't rule out any outliers...because there always are some.

Chopra, K. (2017). Analysis of the mathematical modeling an simulation of advanced marketing in commerce. Journal of Internet Banking and Commerce, 22 (3).