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What Is Causal Forecasting?

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  • Written By: Malcolm Tatum
  • Edited By: Bronwyn Harris
  • Last Modified Date: 19 June 2018
  • Copyright Protected:
    2003-2018
    Conjecture Corporation
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Causal forecasting is a strategy that involves the attempt to predict or forecast future events in the marketplace, based on the range of variables that are likely to influence the future movement within that market. The idea behind this type of prediction is to determine what type of impact those anticipated variables will have on consumer demand, the type of pricing that the market will be able to support in the future, and what those changes would mean for the future of the company. This type of forecasting is helpful to companies in several ways, including the development of sales and advertising for the upcoming period.

There are several elements that go into a causal forecasting model. Typically, the process will begin with an assessment of the market as it currently stands. This will include the current position of the company within that market. From there, there is a need to identify both dependent and independent variables that are likely to exert some influence on the direction that the market will take over a specified period of time. Once there is a reasonable projection of what will happen to the market as a whole, it is possible to apply those same variables and their cumulative effect to the business operation itself.

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One of the benefits of causal forecasting is the ability to prepare for what is most likely to occur in the future. Depending on the outcome of the projections, the company may find it advantageous to begin increasing production now in anticipation of an increased demand for its products at a later date. At the same time, the results of the causal forecasting may indicate upcoming economic circumstances that would make it prudent to begin curtailing production now in order to prevent being left with huge inventories during some sort of recession or other crisis in the market and the general economy.

When the causal forecasting does accurately identify relevant variables and their effects on the market, companies can use the data to protect their interests and have a much better chance of taking advantage of opportunities to grow in the upcoming climate. A thorough forecast will also increase the chances of a company making it through some sort of downturn by allowing for the chance to prepare. In the latter case, this can mean the difference between surviving long enough to see prosperity return to the marketplace or be forced to go out of business before the economic crisis is resolved.

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