Decision making is crucial in leading a company to success. It is also one of the most challenging tasks that have now been made easy through various innovative technologies and methods. Businesses have access to a wide range of information from different sources. This data needs to be processed using scientific analytical methods to get insightful and actionable inputs.
Nowadays, there are many types of quantitative analysis methods, which help to drive business decisions. Some of the most widely used analytical techniques include regression analysis, factor analysis, linear programming, and data mining. Let’s have a deeper look into each.
- Regression analysis
Regression analysis is a popular statistical technique that is used to analyze the impact of independent variables and predictors on the desired outcome of a particular process. For example, economists apply regression analysis to analyze the effect of experience and education on the annual earnings of the workers.
Business managers use regression techniques to estimate the effect of the advertisement expenses and the ROI. Analysis of data from various resources through this technique can ideally determine whether there is any correlation between the elements in question and whether the relationship has any significance in the performance. Thus, the statistical significance relationship is statistically significant!
- Linear programming
Every business faces resource limitations including production equipment, facility space, labor, and supplies. That makes the allocation of these resources challenging and confusing to the administrators. Linear programming is a standard technique used in operation management, which is primarily a mathematical method to determine ways to achieve the maximum desired outcome such as highest profit with lowest possible operating costs. Finweavers, Inc. analytical experts also address the constraints such as limited supplies and labor while adopting linear programming methods.
Analysts and operational researchers in transportation and manufacturing industries make use of linear programming to assess and resolve the problems related to planning and distributing resources.
- Factor analysis
It is a data reduction technique, which is used to survey data and is widely utilized by the market researchers. Factor analysis is highly effective in exploring the correlations between available data components to identify the unmeasured underlying factors, which could probably explain the relationships well.
For example, data analysts use the factor analysis technique to analyze consumer data and their spending habits to identify the general factors which affect specific purchase decision-making models, buying patterns, consumer preferences, and the time-based demand of products.
- Data mining
Data mining is an exceptional combination of computer programming and statistical methods, and it has gained immense popularity lately among business data analysts. As the volume and diversity of the data that is gathered from various resources have grown to a large extent, data mining has evolved as a scientific method of capturing and analyzing large data sets.
With the help of analytical tools like big data analytics, data mining can derive patterns and correlations between masses of data. We can see the e-commerce portals and online retailers like Amazon are using data mining techniques effectively to develop personalized profiles based on the buying patterns of individual customers to plan promotional campaigns and offers.
Quantitative analysis is becoming an essential tool for business success even for the small-scale enterprises. If you are planning to adopt it to gain crucial business insights, then the primary need is to hire a reliable and experienced data consulting service to work on your behalf.