Factor Analysis is used to understand the underlying motives of consumers who buy a product category or a brand i.e. to find out the key factors responsible for purchase of a brand.
For example, assume that a home loan provider is interested in finding out the factors his potential customers think about when they consider taking a home loan. We may have data from customers of a home loan provider on a set of 30 variables relating to attitudes, behavior and preferences with regard to home loans and providers. These statements can then be factor analyzed to come up with let’s say, five factors, which may explain most of the underlying variance in the data.
The data could be scaled data collected from customers, or other types of numeric data about them.
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The objective of Factor Analysis is to find underlying "factors" which are fewer in number than the original set of variables, but would be linear combinations of some of the original variables. It is useful to use the factors instead of the original variables for further marketing mix decisions.
Any industry can use factor analysis. Managers of consumer electronics companies, banks, truck operators, retail stores, or automobile companies can all benefit from the use of factor analysis to understand customer motives in the form of broad underlying factors instead of numerous variables which may actually be measuring the same underlying factors. Using factor analysis helps a marketer to understand his customers and plan his marketing strategy. For instance, a car manufacturer may want to release a small car into the market and understand the factors responsible for purchase of a small car, a cigarette manufacturer may need to determine which variables his potential customers think about when they consider his product, an airline company could want to find out the factors responsible for selection of its airline against its competitor’s airline. All these people would need factor analysis to get them the information they require.
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The process followed takes two distinct steps:
How many factors should be taken from the data? That’s what extraction is all about. This is done based on the Eigen value of all possible factors. Usually, factors with Eigen values of one or higher are extracted in this stage. Another criterion is to maximize the cumulative variance in the extracted factors. About 70% or more may be a cutoff used.
Rotation using one of the orthogonal rotation techniques is then done, and the rotated factor loadings of each factor examined. The variables which load highly on a factor, are identified, and the factor is named after these variables. For example, a set of variables may indicate that a factor called convenience is the underlying theme and forms one factor. Ease of use and reliability of product could be other factors, in a product category such as consumer electronics. The communication strategy may then be devised to portray your brand as convenient to buy, easy to use and long lasting. Similarly, an analysis of a cinema’s customers may indicate that the ambience and the experience of peripheral services (like parking, choice of snacks etc) are the underlying factors that help them decide where to go to the movies.
At the end of this process, the marketing manager has a better understanding of his customers’ needs and motivations. He is able to utilize this knowledge into smarter decisions.
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We use SPSS (Statistical Package for the Social Sciences) to analyze the consumer data and arrive at the underlying factors. We specify the data reduction option in SPSS followed by the criteria for extraction of factors and rotation of the factor matrix. We scan the resulting matrix and deduce the factors and identify them.
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