Statistical analysis has always been a valuable tool for running any business locally or globally. Business executives have to admit that modern market challenges became extremely complex and need to be approached with scientific methods rather than plain intuition. When it comes to business, there are almost uncountable number of ways statistics may come in handy (William).
It is generally known, that the main focus of any business is on markets and people. Therefore, managers try to examine customers’ behavior patterns and make some predictions on how they will react on a wide range of company’s actions and, moreover, to find triggers to influence that behavior for company’s benefit. Since people behavior is at least partially predictable, it makes the application of statistics useful in business practice (Luk, 1998).
Market research is one of the most efficient ways to explore the market and also the most common area of statistical application in business. It is focused on identifying customer needs and finding the conformity between customers’ preferences and tastes to the products or services offered.
Since it is highly impractical and almost unattainable to survey the entire target group of customers that can be counted by thousands and millions of purchasers, statistics offers the way to evaluate their preferences and perceptions by examining a part of the whole target audience - a representative sample. Statistical analysis of a representative sample accurately reflects the market needs and provides reliable snapshot of the market (Luk, 1998).
The results of market research conducted with statistical methods and approaches are frequently used for decision –making and strategy implementation. The statistics allow managers to form an unbiased market perspective and prevent them from flimsy presumptions in strategic planning (William).
Statistics helps to set objective goals supported by scientifically substantiated figures. It also provides firm evidence to justify particular strategic actions and gives an assurance of a certain outcome to all internal and external groups participating in decision-making which includes, but not limited to investors, CEOs and middle-level management.
The overwhelming majority of statistical methods aimed at pointing out the relationships among several variables and explaining the influence of one factor on another. One of the most helpful statistical methodologies that helps investigate the relationships between two or more variables, evaluate the strength and course of their relationships is regression analysis (Nguyen, 2013).
Overall, the regression analysis contributes to revealing the links between several variables and can be used to explain the lion’s part of economic phenomena, such as an effect of specific sales offers on total revenue or dissatisfied customers on products purchased.
However, the most commonly used regressions in business are those that evaluate the influence of the set of factors on the main effectiveness indicator – company’s profit. The total profit may depend on such factors as sales, investment, taxes and statistical analysis was designed to define the relationship among the variables in relative terms.
Moreover, multiple regressions and time series analysis can serve the purpose of business forecasting, which is based on the current relations among factors and assumption that existing trends will remain unchangeable in future.
The necessity of statistics can be clearly observed in continuous improvement or quality assurance programs as Lean Manufacturing and Six Sigma. In this case statistical methods are means to measure and regulate the manufacturing process by minimizing variations, which result in spoilage and guarantee continuity of the production process. In such way, statistical approach helps to save company’s money and boost the productivity (William).
It worth mentioning that although statistical tools are very helpful in running a business they will be work effectively only if applied properly and along with other managerial methods and techniques.
References
Luk, R. (1998). Application of statistics in the business world. Retrieved from http://iase-web.org/documents/papers/icots5/Keynote2.pdf
Nguyen, J. (2013, July 25). Regression basics for business analysis. Retrieved from http://www.investopedia.com/articles/financial-theory/09/regression-analysis-basics-business.asp
William, J.T. The importance of statistics in management decision making. Retrieved from http://smallbusiness.chron.com/importance-statistics-management-decision-making-4589.html
