Big D Incorporated: Use of Multivariate Techniques

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Table of Contents

Introduction
Factor Analysis
Multidimensional Scaling (MDS)
Cluster Analysis
Conclusion and Recommendation
References

Introduction

In multivariate regression, more than one predictive outcome variable is observed and evaluated at the time. In this assignment, the focus will be on multivariate approaches that can best serve Big D Incorporated and their new customers, the outdoor sporting goods clients. This paper will further explain three major ways in which multivariate statistics can be utilized in this scenario, as elaborated in the following paragraphs: factor analysis, multidimensional scaling, and cluster analysis techniques.

Factor Analysis

Factor analysis is a technique in which mass data is taken and simplified into manageable and understandable information. It is a way of searching for unseen patterns, showing how they overlap and revealing characteristics of the data. The approach is useful when analyzing information involving socioeconomic status, psychological studies by creating sets of variables known as dimensions (Shahbaz, Shahbaz, & Hanif, 2019). Big D Incorporated can therefore use factor analysis to evaluate the relationship between quantity variables and their fundamental objective for the business. It will help them reduce huge data available to them and make it understandable with minimal data loss. The approach will promote better data and information association at Big D Incorporated.

Multidimensional Scaling (MDS)

Multidimensional scaling represents the visual illustration of distances or discrepancies among sets of object. In this case, objects can be faces, colors, coordinates of a map, political persuasion, and or conceptual stimuli (Bowerman, O’Connell, Murphee, & Orris, 2016). This approach dictates that similar items tend to be closer together on a graphical representation as opposed to those with divergent similarity. Big D Incorporated has a great opportunity to improve on their performance and customer relations as it will allow them to analyze client perspectives on their products and those of their rivals. In return, the organization will be able to stay ahead of its competitors as well as maintain a grip of the market.

Cluster Analysis

Cluster analysis is used to group a series of objects in a certain way. The technique aims at placing objects, which the data suggests are not defined priority, in groups or clusters, so that objects in a particular cluster tend to be similar in some ways to each other, and objects in other clusters often differ (Jaggia et al., 2016). It is important to note that through this method, Big D Incorporated can do product development and analyze social media clusters, which helps in learning the unknown and unmet needs of the customers.

Conclusion and Recommendation

In conclusion, the use of data to draw meaningful conclusions is a significant idea that companies need to embrace in order to perform well in the market and maintain their customers. The paper recommends that Big D Incorporated uses factor and cluster analysis techniques in its business analysis since the former will help it describe variability that is observable among data sets and correlate them in potentially low numbers to make meaningful interpretations. In addition, it creates a viewpoint that finds solutions for further interpretation and decision-making. On the same note, the latter considers significant population factors such as interests and patterns of consumer purchases, among others, and gives an update prediction on what clients are likely to buy presently and in the future. This will greatly impact Big D Incorporated outdoor sporting goods sales. These two multivariate techniques will best work for the company because of their ability to predict the customers’ perception regarding new goods and services. In addition, these techniques will enable the Board of Directors to make decisions basing on facts from an in-depth analysis of data.

References

Bowerman, T. S., O’Connell, R., Murphee, E., & Orris, J. B. (2016). Essentials of business statistics (5^th ed.). New York, NY: McGraw-Hill Education.

Jaggia, S., Kelly, A., Salzman, S., Olaru, D., Sriananthakumar, S., Beg, R., & Leighton, C. (2016). Essentials of business statistics: Communicating with numbers. New York, NY: McGraw-Hill Education.

Shahbaz, M., Shahbaz, S., & Hanif, M. (2019). Multivariate techniques: An example based approach. Newcastle upon Tyne, England: Cambridge Scholars Publishing.

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Big D Incorporated: Use of Multivariate Techniques

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