Using Statistics for Market Analysis Forecasting

Abstract

Market analysis is a crucial aspect for any organization, business, or company because it provides a ground for decision making. Poor market analysis leads to poor decisions. On the other hand, using quality data to conduct market analysis can provide significant grounds for informed decisions. Business sectors require a clear view of future trends regarding the performance of their products, sales, stocks, employees, and customers, among others. However, defining patterns is possible only through statistical techniques of forecasting. In essence, the knowledge of market analysis forecasting using statistical tools is imperative. This article aims at providing a summary of market forecasting techniques, highlighting their interesting discoveries, and outlining some practical applications in real life. The summary covers regression analysis, handling of special events, identification of seasonality, Holt–Winters method, and forecasting for new products. Regarding regression analysis, it was found that data cleaning is an important aspect of this analysis before the actual forecasting. The data must be tested to meet the reliability and validity criteria to ensure quality data are used for forecasting. The interesting discovery with regard to handling special events was that some special events have great ripple effects, which an organization needs to plan for. Furthermore, when doing an analysis of data, it is essential to take into account the effects of seasonality. It was also ascertained that the accuracy of the Holt–Winters method is associated with its use of smoother curve, which allows a researcher to smooth time series data to make predictions. The article further illustrates that the Bass diffusion model provides more accurate forecasts than logistics and Gompertz models givens its ability to put into consideration the external and internal influence when forecasting sales of new products. One of the applications of this study is that regression models can be used in studying the effectiveness of advertisement platforms during a product marketing campaign. Sales companies can apply seasonality forecasting to understand the influence of different seasons on their products. Moreover, the data on customers’ expenditure patterns can be used to forecast special events to aid in proper planning. Therefore, any business, firm, industry, or country can use forecasting to predict different components of a market.