Factor analysis is a statistical technique in which a multitude of variables is reduced to a lesser number of factors. In the marketing world, it’s used to collectively analyze several successful marketing campaigns to derive common success factors. This, in turn, helps companies understand the customer better.
Factor Analysis & Its Applicability
Factor analysis is used to observe correlated variables and deduce the variability among them by describing it with a few unobserved ‘factors.’ The idea is that the data gathered by observing existing variables can be used to reduce sets of variables in any dataset. That’s why it’s primarily used in machine learning as a method of assisted data mining. Factor analysis is used in fields such as finance, biology, psychology, marketing, operational research, etc.
For example, during inquiries about consumer satisfaction with a product, people may respond similarly to questions about that product’s utility, price, and durability. In any research, the factors and variables are equal in number. Each factor notices a certain variance in each of the observed variables. The ‘eigenvalue’ shows how much variance is observed by a factor. An eigenvalue greater than 1 shows that there is variance in more than one variable on part of the factor.
Therefore, if the ‘happiness with a product’ factor had an eigenvalue of 2.5, it would be the answer to the variance of almost 2.5 of the 3 aforementioned variables. The factor that shows the most variance is then noted. It can be used in future analyses as a reference point. Technically, you can calculate non-tangible and unobservable variables with factor analysis.
Types of Factor Analysis
There are two main types of factor analysis.
Confirmatory Factor Analysis
This technique is used to confirm whether a computed correlation matrix has the right correlations with respect to a certain theoretical factor model.
Confirmatory factor analysis in SPSS is often done to confirm a model with respect to the data entered (keep in mind that you need SPSS Amos to do a confirmatory factor analysis).
This form is used when you already have data on the variables and the factors but need to confirm your assumptions. For example, you may think that your product sales are increasing due to an increase in a competitor’s product price. You can only confirm it; however, through confirmatory factor analysis.
Exploratory Factor Analysis
When you are unaware of the number of factors represented by your data, you ask the software to derive a model based on the input correlation matrix. That is when you explore the data as the software attempts to deduce highly interrelated groups of variables.
Exploratory factor analysis in R is relatively straightforward and can be done with the help of an online guide. R is open-source software for statistical analyses.
One can use this type of factor of analysis when trying to find the underlying reason for a plethora of variables. For example, to find the reason for customer satisfaction, a business may look into their product offerings, packaging, delivery, etc. Exploratory factor analysis deduces the one common factor among all customers, that drives customer satisfaction.
Factor Analysis Techniques
Following are the known factor analysis techniques:
- Common Factor Analysis Evaluates and extracts the common variance in the variables and distributes them into factors. It’s mainly used in SEM and does not necessarily include a unique variance in all of the variables.
- Principal Component Analysis Initiates the extraction of the maximum variance observed and distributes it on the first factor. Then, it undoes its action and extracts the maximum variance for the next factor in the line. The process is repeated for all factors. This method can be used to deduce a target market’s wants and needs in succession.
- Maximum Likelihood Method Works on a correlation metric. This type uses the maximum likelihood method to factor the variables. It can be used to deduce what people want most in a product.
- Image Factoring Works on a correlation matrix. To predict the factor, an OLS Regression method is used. This method can be used to gain an idea of the significance of certain subliminal messages in advertising.
- Other Methods Weight square is used for factoring too. It uses a regression-based method.
Applications of Factor Analysis
Here are two real-life examples of factor analysis in play.
Factor Analysis in Investing
Let’s say you’re a stock trader who wants to invest in a new IPO for a company that specializes in technology. IPO’s are naturally unpredictable and there is no way of knowing whether investing in it will result in a profit.
Investing is a risky business that involves a heavy reliance on data analytics. Usually, people diversify their portfolio to minimize risk, but it still doesn’t eliminate it, and it certainly doesn’t affirm a profit. Many adverse conditions, such as macro and microeconomic shocks, natural disasters, etc. can affect the investment industry.
At this point, you would appreciate the benefits of factor analysis. Factor analysis would anticipate movement and find clues that would otherwise be hidden. Let’s say you invested in the IPO along with another investment in a commodity-based company. In such a situation, a price change of an unrelated variable, such as petrol, may not be properly equated without factor analysis. After thorough exploratory factor analysis, you will be able to deduce the success of the IPO in question.
Factor Analysis in Market Research Surveys
Let’s say you’re a marketer looking to research your market through surveys to determine people’s satisfaction with a product. Satisfaction is hard to quantitate and it’s even harder to determine what factors lead to it.
In the marketing world, factor analysis has been used to develop things such as perception maps, advanced SWOT analyses, etc. It gathers unobservable variables such as customer happiness and quantitates their values against observed variables such as increased sales.
You would design a questionnaire that would focus on questions pertaining to customer satisfaction. This would include variables like product durability, packaging, the chance of reuse. After the customers fill the surveys, you would then do a factor analysis that would extract the variance of each variable and tell you which factor matters the most on customer satisfaction. If you find highly correlated answers for separate variables/questions, you would combine them in a single factor for future analyses.
Research Optimus Can Help Utilize Factor Analysis Techniques
The benefits of factor analysis range from better research data to more accurate statistical research, to the deduction of intangible factors that can only be calculated through thorough analysis. In a world so saturated, accurate data and research is the difference between success and failure. This is why it’s best to hire professionals such as Research Optimus to do your factor analyses for you.