Market Forecasting

It is essential to consider a forecast method to aid in almost all decision$-$making regarding

market analysis. Proper market predictions of future demands and trends are no longer luxurious

options but rather a necessity for a business$$ success. Businesses need to examine sudden

changes and manipulations in price cutting and the market demand levels if market analysis is

incorporated in the decision$-$making of the actions to be taken $($Hyndman & Athanasopoulos,

$2021).$ Many organizations have employed the use of forecasting to make productive market

decisions. If proper decisions are made, then in most cases, the business will achieve the

expected results, leading to success. When there is the availability of historical promotional

response data and sales data for the market mix, an organization can effectively project or predict

future market demands and market swings.

Businesses can utilize qualitative, quantitative, and causal techniques to forecast the

market scenario. Organizations can use qualitative forecasting methods as they are very

subjective and usually rely on the opinions and the judgment of the future demands of the

consumers in the market $($Cai et al$.,2021).$ Qualitative forecasting is effective when there are no

records of the consumer. The forecasting method effectively results in the long$-$term decisions of

the business. Different from the qualitative forecasting method, which is subjective, the

quantitative forecasting method is objective in the decision$-$making process $($Cai et al$.,2021).$ It

is ideal when the consumer$$s historical data is available as it uses numerical historical data made

available by the business rather than relying on assumptions that the business expects future

continuity based on its historical patterns.

Businesses assume that the various underlying patterns stay constant in the quantitative

forecasting methods. Therefore, the organization must use moving averages, exponential

smoothing, the Poisson process model, and multiplicative seasonal indexes when using

quantitative forecasting methods $($Huriati et al$.,2022).$ Causal forecasting methods, unlike

qualitative and quantitative forecasting methods, try to identify the different underlying factors

that lead to a change in the variables that might lead to a change in the forecasted market $($Huriati

et al$.,2022).$ For instance, causal forecasting methods consider external environmental factors,

including climate change patterns, in predicting the future.

Concerning the given scenario, the forecasting methods that can be effectively used are

majorly causal and quantitative. Since all the historical data have been provided, quantitative

forecasting methods can likely be used to project the market. A business would opt for

quantitative methods since they are modeled and effectively project future sales in the market.

Additionally, the business can use the causal forecasting method as it is likely to emphasize

future market projections and sales. However, using another forecasting method might mislead

the business projection, thus putting it in a position where it has much to lose.

Therefore, since historical promotional response data and sales data are available for the

market mix, the most relevant forecasting method in this scenario is quantitative. The

organization might not settle for the qualitative method as it is effective when there is no

historical data. Hence, it might not be practical in this scenario due to the availability of

historical data. For instance, the Poisson process model or moving averages can model historical

sales data and the final data employed in predicting the market mix. Moreover, the organization

can tabulate the promotional response data to predict future business trends and market swings.

As stated earlier, quantitative methods are effective in scenarios with past historical data records,

thus ideal in market forecasting.