What is Factor analysis?
Factor analysis is a statistical technique used by market researchers and data analysts to analyze complicated data by narrowing it down into a number of smaller factors. Complex unstructured data which is difficult to interpret is reduced to small factors by understanding their commonality and trend.
While conducting consumer surveys, customer satisfaction is difficult to calculate directly as it’s a phycological choice of the individual consumer. Hence with the help of factor analysis, complicated survey results are broken down into factors such as durability and utility of the product, price, and availability which can be measured to understand customer satisfaction. This technique is not only related to the marketing survey but is extensively used in a wide range of industries that handle complex data such as finance, healthcare, marketing, and operational research.
With the growing investment and adoption of technology, factor analysis is also used in machine learning. Machine learning is increasingly adopted to understand the pattern and correlation of data. Factor analysis programs, along with other techniques are fed into the machine learning algorithms this helps to organize, arrange and study the factors of a complex dataset. There is an abundance of data in the world we know today and significant time and resources are invested in data mining technology which helps to find the right data for a study or a hypothesis. Factor analysis due to its promising application of transforming complex data sets into a simple hypothesis is increasingly used in data mining technology.
Types of factor analysis:
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Confirmatory factor analysis
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Exploratory factor analysis
Confirmatory factor analysis is used to understand the relation and correlation between the different factors in a large pool of data. When the factors in the data set are known and there is an existing hypothesis on the data trends and correlation confirmatory factor analysis is used. For instance, the sales of a company’s products might be related to a decrease in the product price or an increase in the competitors’ prices. These correlations can be proven by analyzing the dataset with confirmatory force factors. This technique can be implemented in a wide range of data sets such as understanding the impact of a pharmaceutical drug among the different pools of patients
Exploratory factor analysis on the other hand is used when the factors and the correlations are unknown. The system fed with this technique runs the pool of data to connect the dots and establish the relation between varying data points or factors.
Advantages of using factor analysis:
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Wider application
Quantification of subjective factors can be done with the help of factor analysis. Market research, consumer research, and data analysts’ studies not only objective factors in the target pool but also subjective factors. More often than none subjective understanding does not help the analysts reach a conclusive decision backed by data, as these factors are difficult to quantify. Factor analysis helps to quantify, correlate and measure not only objective attributes but subjective attributes as well.
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Economical than automation tools
Due to the abundance of data companies are investing significant resources in implementing data mining algorithms and tools. However, using factor analysis statistical tools in the absence of expensive data mining technology helps to reduce the research budget of the organization or individual.
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Flexible methodology
Data mining and machine learning are automation tools and can process the data based on the specific logic and factors implemented in the algorithms used. While is factor analysis is a flexible statistical tool that can be used to study the data from different perspectives and approaches.
Author's Detail:
Kalyani Raje / Linkedin
With a work experience of over 8+ years in the market research and strategy development. I have worked with diverse industries, including FMCG, IT, Telecom, Automotive, Electronics and many others. I also work closely with other departments such as report writing, content writing, product development, and marketing to understand customer needs and preferences, and develop strategies to meet those needs.
Author's Detail:
Kalyani Raje /
LinkedIn
With a work experience of over 10+ years in the market research and strategy development. I have worked with diverse industries, including FMCG, IT, Telecom, Automotive, Electronics and many others. I also work closely with other departments such as sales, product development, and marketing to understand customer needs and preferences, and develop strategies to meet those needs.
I am committed to staying ahead in the rapidly evolving field of research and analysis. This involves regularly attending conferences, participating in webinars, and pursuing additional certifications to enhance my skill set. I played a crucial role in conducting market research and competitive analysis. I have a proven track record of distilling complex datasets into clear, concise reports that have guided key business initiatives. Collaborating closely with multidisciplinary teams, I contributed to the development of innovative solutions grounded in thorough research and analysis.
