Quantitative Research Use in Business and Marketing

Research is key to the success of any industry. There comes a time when each sector has to conduct a research, no matter the scope, to enable the development of more informed decisions as well as lay out strategic plans. The scope of research comes in two distinct categories, namely quantitative and qualitative research. A clear distinction exists between the two. As asserted by Barnham (2015), quantitative research entails more of factual and hard data while qualitative research provides a better insight rather than being interpretative. Each of these has its use in different fields. Focussing on quantitative research design, there exist various research tools that can be applied in real life situations in any field, say business, technology, or even health fields. This text examines how quantitative research and its various tools can be applied in decision-making in business and marketing.

 Decision-making is the key to the overall success and functioning of any business entity. Business decisions are arrived at through a well-conducted research. Quantitative research methods come with various research and analysis tools that come in handy in providing insights and conclusions for business decision-making. As Barnham (2015) explains, quantitative data is collected in various ways. Some of the most popular approached include measurements, quantitative surveys and interviews, use of questionnaires, and experiments. One key element to note about quantitative methods and tools is that they adopt a numerical approach (Barnham, 2015). Thus, the methods would be mathematical and statistical in nature.

Quantitative Research Methods

The first method under quantitative research is regression analysis. Regression analysis establishes a causal relationship between two or more variable of interest in decision-making (Baker and Hart, 2008). This technique is commonly used in almost every business situation. Sets of data collected are categorized into dependent and independent variables that are then analyzed to show the effect of the predictor on the independent variable. For instance, regression analysis may be used to establish the effects of high tax rates on the revenues of business, salary increments on the overall profits, advertisements and market promotions on the amount of sales, and such. This method uses the real numerical data collected by a business entity in its operations and research, and thus, tends to be more accurate in establishing correlational observations. Managers can use the regression analysis tool to predict the possible outcomes for different scenarios; for example, if previous analysis shows that more advertisement leads to more sales, would the same apply in future?

            Additionally, factor analysis is another quantitative tool used for decision making in business and marketing. Under this technique, data reduction is performed on survey data collected by the business and market researchers of an organization (Yong and Pearce, 2013). Correlations are analyzed for the data and observations to unearth factors that form the relations. One real-life example is the use of factor analysis by marketers in data analysis on the consumer spending patterns. Such an analysis helps the marketers in understanding given purchasing trends, which by large can assist the business represented in knowing the consumers’ product preference. This is effective in meeting specific customers’ needs and tastes.

            Consequently, quantitative research also entails another approach in the decision-making process, which is linear programming. Research on resource allocation for businesses is always a challenge since a crucial decision must be made on the proportion of the allocation. Linear programming uses the quantitative data and approach to establishing the optimal allocation decision, for instance, the amount of funds that should be allocated for advertising versus distribution or any other element within business and marketing (Baker and Hart, 2008). With linear programming, business managers can come up with highest profits under the lowest operations costs. The marketing departments can also learn to manage constraints such as limited supplies and labor to solve issues related to planning, sales, and such.

            From the above discussion on the various tools and their use in decision making in business and marketing, it is possible to deduce possible facts about the future of quantitative research. As opposed to the qualitative approach, quantitative research has a bigger usage in the day-to-day business applications. The business and marketing sector depends largely on numerical data and statistics in making crucial decisions and plans. As such, a quantitative research analysis is more likely to give better predictions relating to marketing and business performance through its various tools. More people will tend to adopt quantitative research methods and tools in future business and marketing research. New ways have already been adopted to make quantitative research more effective, and the future will see more sophisticated applications in all sectors.

Quantitative research can now be done over the Internet, through online surveys, data mining, among others. Quantitative research analysis has also seen a new turn whereby data collected for forecasting can now be interpreted through complex computer applications. With the changing technology and more inventions, quantitative research will adopt more efficient approaches for arriving at conclusions and making more informed decisions. Business and marketing experts will employ systems that will make use of the daily changes that take place within the economy to capture all elements without going to the field to collect data. The analysis will be instantaneous, and decision-making will be based solely on the outcomes of the analysis provided by the quantitative systems.


Baker, M. & Hart, S. (2008). The Marketing Book (6th ed., pp. 170-220). Oxford: Elsevier Ltd.

Barnham, C. (2015). Quantitative and Qualitative Research. International Journal of Market Research,57(6), 837-854. http://dx.doi.org/doi:10.2501/IJMR-2015-070

Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology9(2), 79-94.

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