Market research and analysis of large volumes of data are necessary when it comes to analyzing and determining the right market segment, potential demand, and potential areas of competition, product development requirements and all other facets of the business marketing portfolio. One of the most common tools used to deal with the vast amounts of data is Factor Analysis. Factor analysis, in layman’s terms, is used to analyze the relationship between two observable variables and how it is affected by another smaller set of unobservable variables. For example, factor analysis can be used in market segmentation to identify the underlying variables according to which customers can be grouped.
Uses of factor analysis in market research and analysis
Factor analysis has proved to be very beneficial in market research and analysis of variables that determine consumer behavior:
- It helps to make sense of large data with interlinked relationships
- It may point out relationships that may not have been obvious
- It can point out to the underlying relationships with respect to consumer tastes, preferences, etc.
- It is easier to condense and correlate data through factor analysis and also to draw conclusions from the data gathered in market research and analysis.
- It can be used to form empirical clusters of variables and underlying factors that affect them
Types of factor analysis
A factor analysis is mainly used for interpretation of data and in analyzing the underlying relationships between variable and other underlying factors that may determine consumer behavior. Instead of grouping responses and response types, factor analysis segregates the variable and groups these according to their co relevance.
There are mainly three types of factor analysis that are used for different kinds of market research and analysis.
- Exploratory factor analysis
- Confirmatory factor analysis
- Structural equation modeling
Exploratory factor analysis is used to measure the underlying factors that affect the variables in a data structure without setting any predefined structure to the outcome. Confirmatory factor analysis on the other hand is used as tool in market research and analysis to reconfirm the effects and correlation of an existing set of predetermined factors and variables that affect these factors. Structural equation modeling hypothesizes a relationship between a set of variables and factors and tests these casual relationships on the linear equation model. Structural equation modeling can be used for exploratory and confirmatory modeling alike, and hence it can be used for confirming results as well as testing hypotheses.
Factor analysis will only yield accurate and useful results if done by a researcher who has adequate knowledge to select data and assign attributes. Selecting factors and variables so as to avoid too much similarity of characteristics is also important. Factor analysis, if done correctly, can allow for market research and analysis that helps in various areas of decision making like product features, product development, pricing, market segmentation, penetration and even with targeting.
- Crosstab vs. List: Pros, Cons and Best Uses
- Top Do’s and Don’ts for Survey Data Visualization
- Crosstab Software and the Importance of Referential Integrity
- Visualizing Survey Data: How to Turn Your Data into a Stunning Infographic
- Crosstab Software: Data Normalization Tips
- Visualizing Survey Data for Net Promoter Score Analysis
- Crosstab Software: What You Need to Know About Your Data Before You Begin
- Using Stacked Bar Charts to Visualize Survey Data
- Crosstab Reports: Uses and Tips
- Visualizing Survey Data for Employee Performance Surveys